Using historical data for subrogation on a distributed ledger

ABSTRACT

Systems and methods are disclosed with respect to using a blockchain for managing the subrogation claim process related to a vehicle accident, in particular, utilizing historical data related to a vehicle or vehicle collisions as part of the subrogation process. An exemplary embodiment may include receiving historical sensor data, such as image, audio, telematics, and/or autonomous vehicle data, associated with a past vehicle collision; inputting the historical sensor data into a machine learning program to determine data relevant to a past vehicle collision; receiving current sensor data associated with a current vehicle collision; inputting the current sensor data into the machine learning program to determine data relevant to the current vehicle collision; and determining a percentage of fault of the vehicle collision for one or more autonomous vehicles, autonomous vehicle systems, and/or drivers based upon, at least in part, analysis of the historical sensor data and the current sensor data.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/999,260, filed Aug. 21, 2020, which claims priority to (1) U.S.patent application Ser. No. 15/957,438, entitled “Using Historical Datafor Subrogation on a Distributed Ledger,” which claims priority to (2)U.S. Provisional Application No. 62/555,030, entitled “Using aBlockchain for the Subrogation Claim Process”, and filed Sep. 6, 2017;(3) U.S. Provisional Application No. 62/554,907, entitled“Blockchain-Based Claim Handling”, and filed Sep. 6, 2017; (4) U.S.Provisional Application No. 62/555,358, entitled “Using a Blockchain forthe Subrogation Claim Process”, and filed Sep. 7, 2017; and (5) U.S.Provisional Application No. 62/609,644, entitled “Using Historical Datafor Subrogation on a Distributed Ledger”, and filed Dec. 22, 2017, eachof which is hereby incorporated herein by reference in its entirety.

TECHNICAL FIELD

Systems and methods are disclosed with respect to using a blockchain formanaging the subrogation claim process related to a vehicle accident, inparticular, utilizing historical data related to a vehicle or vehiclecollisions as part of the subrogation process.

BACKGROUND

The insurance claim process may involve a tremendous number ofcommunications and interactions between parties involved in the process.Potential parties to the claim process may be insurance companies,repair shops, lawyers, arbitrators, government agencies, hospitals,drivers, and collection/collections agency. Sometimes the costs ofrepairs may be disputed and parties may pursue subrogation forparticular charges. As an example, when an insured person suffers acovered loss, an insurer may pay costs to the insured person and pursuesubrogation from another party involved in the loss. If an insuredvehicle is involved in a collision and suffers a loss, the insurer maycompensate the vehicle owner according to an insurance agreement. If,for example, the vehicle owner was not at fault in the collision, theinsurer may pursue damages from another party, such as the insurer ofthe party who was at fault in the collision. An insurance agreement mayinclude an obligation of an insured to assign the insured's claimagainst a party at fault to the insurer, who may then collect on theclaim on the insured's behalf.

Settling a subrogation payment may be a lengthy, complicated process.The various parties (e.g., parties at fault in a vehicle collision,owners of the vehicles, insurers, etc.) may need to exchange informationrelating to the collision to determine which party was at fault. Sourcesof information relevant to a fault information and/or subrogationpayment may include information regarding parties involved in a loss,forensic data regarding the loss, vehicle data regarding a loss, etc.The various parties may verify and share information from a variety ofsources, including information held by parties involved in a loss andtheir insurers, and information obtained from third parties (e.g.,governmental entities, independent contractors, etc.).

The parties to a subrogation payment (e.g., insurers) may make proposalsto one another to settle the subrogation claim. A proposal may includean accounting of damages, such as the costs to a vehicle owner whosevehicle was damaged. If an insured person suffered an injury in acollision, the injured person's health care costs may be included in theaccounting of damages. One or both of the parties to a subrogation claimmay rely on independent third parties to assess costs, such as a repaircost estimate by an authorized automotive repair services provider fordamage incurred in a collision. To settle the subrogation claim, theparties may indicate acceptance or approval of damages calculations, anda payment amount agreed upon between the parties to settle the claim.Parties may rely on a third-party intermediary to handle subrogationnegotiations and resolution (e.g., validate information relating to aloss and facilitating communications between the insurers) at addedexpense.

BRIEF SUMMARY

Systems and methods are disclosed for utilizing a distributed ledger, orblockchain, to manage an insurance claim process, in particular, asubrogation claim process. The systems and methods disclose usingevidence oracles for inputting information into the blockchain,utilizing machine learning to suggest amounts for the subrogationprocess, a line item dispute mechanism, and creating/managing adistributed ledger in response to a vehicle being in an collision. Themethods and systems may make use of secure transactions and smartcontracts stored on the blockchain.

The present embodiments further may relate to insurance and handlinginsurance claims. Sensor, image, or other data may be collected fromvarious sources, such as mobile devices, one or more vehicles (such assmart or autonomous vehicles, or autonomous or semi-autonomous vehiclesystems), smart infrastructure, satellites, drones, and/or smart orinterconnected homes. The data collected may be analyzed by artificialintelligence or machine learning algorithms to (1) identify whether avehicle collision occurred; (2) determine a percentage of fault (for thedrivers or autonomous vehicles or systems); (3) determine the veracityof an insurance claim or identify potential fraud or buildup; (4)facilitate subrogation or arbitration processes; (5) determine andassign liability to vehicle manufacturers or drivers; (6) create newblockchains and/or individual blocks for blockchains associated with aparticular insurance claim, individual, or vehicle; (7) provide paymentsor e-payments among parties; and/or (8) facilitate other functionalitydiscussed herein.

In one aspect, a computer-implemented method for handling or processingan insurance claim via a shared ledger may be provided. The method mayinclude, via one or more local or remote processors, servers, sensors,and/or associated transceivers: (1) receiving, at one or moreprocessors, historical sensor data associated with a past vehiclecollision; (2) receiving, at the one or more processors, current sensordata associated with a vehicle collision, such as from one or moreinterconnected sources; (3) determining, at the one or more processors,a percentage of fault of the vehicle collision for one or more vehicles,vehicle systems, and/or drivers based upon, at least in part, analysisof the historical sensor data and the current sensor data; and (4)creating, at the one or more processors, a blockchain for the vehiclecollision with one or more links to the sensor data collected, and anindication of the percentage of fault determined to facilitateblockchain-based claim handling. The method may include additional,less, or alternate actions, including those discussed elsewhere herein.

In another aspect, a computer-implemented method for handling orprocessing an insurance claim via a shared ledger may be provided. Themethod may include, via one or more local or remote processors, servers,sensors, and/or associated transceivers: (1) receiving, at one or moreprocessors, historical sensor data associated with a past vehiclecollision; (2) inputting, at the one or more processors, the historicalsensor data into a machine learning program to determine data relevantto a past vehicle collision; (3) receiving, at the one or moreprocessors, current sensor data associated with a current vehiclecollision, such as from one or more interconnected sources; (4)inputting, at the one or more processors, the current sensor data intothe machine learning program to determine data relevant to the currentvehicle collision; (5) determining, at the one or more processors, apercentage of fault of the vehicle collision for one or more vehicles,vehicle systems, and/or drivers based upon, at least in part, analysisof the historical sensor data and the current sensor data; and (6)creating, at the one or more processors, a blockchain for the vehiclecollision with one or more links to the sensor data collected, and anindication of the percentage of fault determined to facilitateblockchain-based claim handling. The method may include additional,less, or alternate actions, including those discussed elsewhere herein.

In yet another aspect, a computer system configured to handle or processan insurance claim via a shared ledger may be provided. The system mayinclude one or more processors, servers, sensors, and/or associatedtransceivers configured to: (1) receive historical sensor dataassociated with a past vehicle collision; (2) input the historicalsensor data into a machine learning program to determine data relevantto a past vehicle collision; (3) receive current sensor data associatedwith a current vehicle collision, such as from one or more sources, suchas those shown in FIG. 11 ; (4) input the current sensor data into themachine learning program to determine data relevant to the currentvehicle collision; (5) determine a percentage of fault of the vehiclecollision for one or more vehicles, vehicle systems, or drivers basedupon, at least in part, analysis of the historical sensor data and thecurrent sensor data; and (6) create a blockchain for the vehiclecollision with one or more links to the sensor data collected, and anindication of the percentage of fault determined to facilitateblockchain-based claim handling. The system may include additional,less, or alternate components and actions, including those discussedelsewhere herein.

The methods may be implemented via computer systems, and may includeadditional, less, or alternate actions or functionality. Systems orcomputer-readable media storing instructions for implementing all orpart of the method described above may also be provided in some aspects.Systems for implementing such methods may include one or more of thefollowing: a special-purpose computing device, a personal electronicdevice, a mobile device, a wearable device, a processing unit of avehicle, a remote server, one or more sensors, one or more communicationmodules configured to communicate wirelessly via radio links, radiofrequency links, and/or wireless communication channels, and/or one ormore program memories coupled to one or more processors of the personalelectronic device, processing unit of the vehicle, or remote server.Such program memories may store instructions to cause the one or moreprocessors to implement part or all of the method described above.Additional or alternative features described herein below may beincluded in some aspects.

This summary is provided to introduce a selection of concepts in asimplified form that are further described in the Detailed Descriptions.This summary is not intended to identify key features or essentialfeatures of the claimed subject matter, nor is it intended to be used tolimit the scope of the claimed subject matter.

Advantages will become more apparent to those of ordinary skill in theart from the following description of the preferred aspects, which havebeen shown and described by way of illustration. As will be realized,the present aspects may be capable of other and different aspects, andtheir details are capable of modification in various respects.Accordingly, the drawings and description are to be regarded asillustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The figures described below depict various aspects of the system andmethods disclosed herein. It should be understood that each figuredepicts an embodiment of a particular aspect of the disclosed system andmethods, and that each of the figures is intended to accord with apossible embodiment thereof. Further, wherever possible, the followingdescription refers to the reference numerals included in the followingfigures, in which features depicted in multiple figures are designatedwith consistent reference numerals.

There are shown in the drawings arrangements which are presentlydiscussed, it being understood, however, that the present embodimentsare not limited to the precise arrangements and are instrumentalitiesshown, wherein:

FIG. 1 is a schematic diagram of an exemplary shared ledger system formanaging and resolving subrogation claims with arbitration, inaccordance with one aspect of the present disclosure;

FIG. 2 depicts an exemplary shared ledger system for resolvingsubrogation claims, in accordance with one aspect of the presentdisclosure;

FIG. 3 depicts exemplary validating network nodes and an exemplarytransaction flow on a shared ledger network for resolving subrogationclaims, in accordance with one aspect of the present disclosure;

FIG. 4 depicts exemplary components of a network node on a shared ledgernetwork for resolving subrogation claims, in accordance with one aspectof the present disclosure;

FIG. 5 depicts an exemplary smart contract state in a shared ledgernetwork for resolving subrogation claims, in accordance with one aspectof the present disclosure;

FIG. 6 depicts an exemplary transaction representing an evidencetransaction generated by an evidence oracle associated with one aspectof the present disclosure;

FIG. 7 depicts an exemplary flow diagram for providing data relevant tocollisions and subrogation claims by interacting with a distributedledger;

FIG. 8 depicts an exemplary flow diagram for generating suggestedsubrogation amounts using machine learning;

FIG. 9 depicts an exemplary flow diagram for providing a line itemdispute mechanism related to a subrogation claim;

FIG. 10 depicts an exemplary flow diagram for interacting with adistributed ledger to create subrogation claims related to a vehicleaccident;

FIG. 11 depicts various sources of sensor, image, audio, and/ortelematics data used to initiate blockchain creation for vehiclecollisions; and

FIGS. 12-15 depict exemplary computer-implemented methods of handling aninsurance claim via a shared ledger.

The Figures depict preferred embodiments for purposes of illustrationonly. One skilled in the art will readily recognize from the followingdiscussion that alternative embodiments of the systems and methodsillustrated herein may be employed without departing from the principlesof the invention described herein.

DETAILED DESCRIPTION

Traditionally, businesses, customers, and central authorities, such asthose involved in an insurance claim, have stored information related totransactions, and records of transactions, in databases or ledgers.Often these databases and ledgers are held by the participants and maybe reconciled to achieve consensus as to the validity of the informationstored therein. Alternatively, a central authority may be responsiblefor determining the validity of information stored in a database or aledger, and functioning as an arbiter of consensus for interestedparties, such as a recorder of deeds, an asset exchange, etc.

A blockchain (also referred to herein as a distributed ledger or ashared ledger) may be a way of achieving a distributed consensus on thevalidity or invalidity of information in the chain. In other words, theblockchain may provide a decentralized trust to participants andobservers. As opposed to relying on a central authority, a blockchainmay be a decentralized database in which a transactional record ofchanges to the ledger may be maintained and validated by each node of apeer-to-peer network. The distributed ledger may be comprised ofgroupings of transactions organized together into a “block,” and orderedsequentially (thus the term “blockchain”). Nodes may join and leave theblockchain network over time and may obtain blocks that were propagatedwhile the node was gone from peer nodes. Nodes may maintain addresses ofother nodes and exchange addresses of known nodes with one another tofacilitate the propagation of new information across the network in adecentralized, peer-to-peer manner.

The nodes that share the ledger form what is referred to herein thedistributed ledger network. The nodes in the distributed ledger networkmay validate changes to the blockchain (e.g., when a new transactionand/or block is created) according to a set of consensus rules. Theconsensus rules may depend on the information being tracked by theblockchain, and may include rules regarding the chain itself. Forexample, a consensus rule may include that the originator of a changesupply a proof-of-identity, such that only approved entities mayoriginate changes to the chain. A consensus rule may require that blocksand transactions adhere to format requirement and supply certain metainformation regarding the change (e.g., blocks must be below a sizelimit, transactions must include a number of fields, etc.). Consensusrules may include a mechanism to determine the order in which new blocksare added to the chain (e.g., through a proof-of-work system,proof-of-stake, etc.).

Additions to the blockchain that satisfy the consensus rules may bepropagated from nodes that have validated the addition to other nodesthat the validating node is aware of. If all the nodes that receive achange to the blockchain validate the new block, then the distributedledger may reflect the new change as stored on all nodes, and it may besaid that distributed consensus has been reached with respect to the newblock and the information contained therein. Any change that does notsatisfy the consensus rule may be disregarded by validating nodes thatreceive the change, and may not propagated to other nodes. Accordingly,unlike a traditional system which uses a central authority, a singleparty cannot unilaterally alter the distributed ledger unless the singleparty can do so in a way that satisfies the consensus rules. Theinability to modify past transactions leads to blockchains beinggenerally described as trusted, secure, and immutable. Third partyintermediaries who assist in the resolution of subrogation claims maythus be disintermediated from the process by a decentralized blockchain.

The validation activities of nodes applying consensus rules on ablockchain network may take various forms. In one implementation, theblockchain may be viewed as a shared spreadsheet that tracks data suchas the ownership of assets. In another implementation, the validatingnodes may execute code contained in “smart contracts” and distributedconsensus may be expressed as the network nodes agreeing on the outputof the executed code.

A smart contract may be a computer protocol that enables the automaticexecution and/or enforcement of an agreement between different parties.In particular, the smart contract may be computer code that is locatedat a particular address on the blockchain. In some cases, the smartcontract may run automatically in response to a participant in theblockchain sending funds (e.g., a cryptocurrency such as bitcoin, ether,or other digital/virtual currency) to the address where the smartcontract is stored. Additionally, smart contracts may maintain a balanceof the amount of funds that are stored at their address. In somescenarios, when this balance reaches zero the smart contract may nolonger be operational.

The smart contract may include one or more trigger conditions, that,when satisfied, correspond to one or more actions. For some smartcontracts, which action(s) from the one or more actions are performedmay be determined based upon one or more decision conditions. In someinstances, data streams may be routed to the smart contract so that thesmart contract may detect that a trigger condition has occurred, and/oranalyze a decision condition.

Blockchains may be deployed in a public, decentralized, andpermissionless manner meaning that any party may view the shared ledger,submit new information to be added to the ledger, and/or join thenetwork as a validating node. Other blockchains may be private (e.g.,permissioned ledgers) that keep chain data private among a group ofentities authorized to participate in the blockchain network. Otherblockchain implementations may be both permissioned and permissionlesswhereby participants may need to be validated, but only the informationthat participants in the network wish to be public is made public.

The present embodiments relate to, inter alia, systems and methods forusing a blockchain to record and manage information related toresolution of a subrogation claim (e.g., a “subrogation” blockchain).The subrogation blockchain may be either a public or permissionedledger. In particular, certain embodiments discussed herein relate tothe use of “evidence oracles,” as part of a subrogation blockchain.These evidence oracles may interact with the blockchain and sendinformation, such as evidence related to a subrogation claim, to theblockchain.

Exemplary Shared Ledger for Resolving Subrogation Claims

FIG. 1 depicts an exemplary shared ledger system 100 for resolvinginsurance claims, and more particularly, subrogation claims inaccordance with one aspect of the present disclosure. Participants inthe shared ledger system 100 may be insurance companies 106 and 108,repair shops 122, lawyers 144, arbitrators 140, government agencies 124,hospitals 120, vehicles 102 and 104, collection/collections agency 134,and evidence oracles 148. The claims process may be problematic in manyregards, such as identifying what evidence is related to, and valid for,a collision, determining proper subrogation amounts, and/or negotiatingwhat line items each party is responsible for.

These problems, and others addressed herein, may be obviated through theuse of a distributed ledger. For example, evidence oracles that recordinformation occurring in the physical world may transmit thatinformation to a distributed ledger where it can be used in the claimsprocess. An automated recommendation engine powered by artificialintelligence may be used to suggest subrogation payment amounts. Theline item negotiation may be done through transactions on the blockchainwhere all the necessary parties can see the information. Lastly, tofacilitate the claims process, a connected vehicle involved in a crashmay create its own distributed ledger, or update an existing distributedledger with relevant information to the claims process. These solutionsmay be used for the claims process generally, or more particularly, fora subrogation claim process.

For a subrogation claim, when an insured party, such as the owner ofnot-at-fault vehicle 104 experiences a covered loss, for example in acollision with at-fault vehicle 102, the owner of not-at-fault vehicle104 may submit an insurance claim 110 to an insurer 106. The insurer 106may have a contractual obligation to remit a payment 112 (such as cryptoor digital currency) to the owner of the not-at-fault vehicle inexchange for assignment of any legal claim the owner of not-at-faultvehicle may have against the owner or operator of at-fault vehicle 102for damage and expenses associated with the collision.

After insurer 106 has remitted payment 112 (such as real dollars, or acrypto or digital currency) to the owner of the not-at-fault vehicle 104and receive assignment of the vehicle owner's claim against the owner oroperator of at-fault vehicle 102, the insurer 106 may initiate a processof managing and resolving the legal claim against the owner or operatorof the at-fault vehicle 102 or against an insurer 108 of the at-faultvehicle (e.g., a subrogation claim). The shared ledger system 100 mayinclude a blockchain 118 accessible by network participants via anetwork 116 (e.g., a private or public packet switched network). Tobegin the blockchain subrogation claim resolution process, the insurer106 may broadcast a subrogation claim or transaction 114 to theblockchain 118.

The blockchain 118 may be a network wherein participating network nodesvalidate changes to a ledger based upon transactions broadcast by othernetwork participants. The transaction 114 may include informationrelating to the subrogation claim that may be modified by subsequenttransactions broadcast over the network 116. In another implementation,validators on the blockchain 118 may be configured to maintain a statedatabase and execute code in smart contracts deployed by networkparticipants. A smart contract on the blockchain 118 may expose methodsand maintain the state of data relating to a subrogation claim by theinsurer 106 against the insurer 108 relating to an insured loss coveredby the insurer 106.

After a subrogation claim 114 has been lodged by insurer 106,information relating to the covered loss may be collected by otherparties that were involved in the collision to evaluate the subrogationclaim. For example, a hospital 120 may broadcast a damages transaction126 to the blockchain 118 that may include evidence of medical billsincurred as part of the collision. An automotive services repairprovider 122 may broadcast a transaction 128 to supply informationregarding repair services estimated or rendered as a result of thecollision. A government entity 124 may supply evidence relating to thecollision in vehicle transaction 130. Vehicle transaction 130 mayinclude information relating to one or more of the vehicles 102 and 104involved in the collision relevant to the subrogation claim (e.g.,registration status, registered owner, legal title information, policereports regarding the collision, driving records of the drivers involvedin the collision, lien information regarding the vehicles 102 and 104,etc.).

Evidence may also be supplied by evidence oracles 148 through the use ofevidence transactions 150. These evidence oracles may be devicesconnected to the internet that record information about the physicalenvironment around them. For example, the evidence oracles may beconnected video cameras, traffic cameras, road sensors, motion sensors,environmental conditions sensors (e.g., measuring atmospheric pressure,humidity, etc.), as well as other Internet of Things (IoT) devices. Theoracles 148 may package their data into transactions 150 that aresubmitted to a blockchain.

When entities broadcast transactions to the blockchain 118 to initiateor add data to a subrogation claim, the transactions may be accompaniedby a proof-of-identity of the entity broadcasting the transaction. Inone implementation, a cryptographic proof-of-identity may be included intransactions sent to the blockchain. For example, each of the entities106, 108, 120, 122, and 124 may own private cryptographic keys that areassociated with public cryptographic keys known to belong to the entity(e.g., public cryptographic keys associated with each of the entitiesmay be published by a trusted third party or proven to other networkparticipants, etc.).

An entity wishing to broadcast a transaction to the blockchain 118 maysign a cryptographic message in the transaction with the entity'sprivate cryptographic key to prove the identity of the entitybroadcasting the transaction. In this way, other network participantsmay be provided with cryptographic proof that the information containedin the broadcast transaction was originated by the participating entity.

After entities such as the hospital 120, automotive repair servicesprovider 122, government agency 124, etc. have supplied informationrelevant to the subrogation claim, the subrogation claim defendantinsurer 108 may broadcast one or more subrogation transactions 132 tothe blockchain 118 to indicate acceptance or rejection of the variouscomponents of the subrogation claim. For example, if the subrogationclaim defendant 108 disputes that medical care provided by the hospital120 was caused by the collision, and thus would form a proper basis forliability to the subrogation claimant insurer 106, the subrogationdefendant insurer 108 may broadcast a transaction marking the damagestransaction 126 as disputed or not agreed. In response, the insurer 106may broadcast a transaction 114 to respond to the subrogation defendant108's rejection, such as lowering the damages claimed as part of themedical costs incurred at the hospital 120, adding more evidence of thenature of the medical services rendered by the hospital 120, etc.

If the subrogation defendant insurer 108 accepts the damages associatedwith the subrogation claim brought by the claimant insurer 106, thesubrogation defendant insurer 108 may broadcast a transaction 132 to theblockchain 118 to indicate a resolution of the subrogation claim for theamount reflected by the blockchain 118. Alternatively, or additionally,the subrogation defendant insurer 108 may broadcast a transaction to theblockchain 118 reflecting payment of the subrogation claim (includingeither real dollars, or a crypto or digital currency), and/or maybroadcast a transaction sending a token having value that circulates onthe blockchain 118 to the insurer claimant 106.

Alternatively, one or more of the parties to the subrogation claim(e.g., the insurers, the parties to the loss, etc.) may demandresolution of the subrogation claim by a third party. In someimplementations, parties may indicate lawyers 144 are desired to advancethe subrogation claim, such as in a court of law for example. In anotherimplementation, a third party who may resolve the subrogation claim onthe blockchain 118 may be an arbitrator 140.

Another third party that may be involved in the subrogation claim on theblockchain 118 may be collection/collections agency 134. Upon resolutionof a claim, the prevailing party may experience difficulty in collectingany judgment owed. As such, the prevailing party may indicate interestin debt collection services from collection/collections agency 134 inmuch the same way as the parties indicate desire for arbitration, or forlawyers to take the case.

Exemplary Validating Nodes in a Distributed Ledger System for ManagingInsurance Claims

FIG. 2 depicts an exemplary shared ledger system 200 for managinginsurance claims, including subrogation claims in accordance with oneaspect of the present disclosure. The system 200 may include a shareddistributed ledger 212, and plurality of nodes 202, 204, 206, 208, and210. Each node may maintain a copy of the distributed ledger 212. Aschanges are made to the distributed ledger 212, each node received thechange via network 214 may update its respective copy of the shareddistributed ledger 212. A consensus mechanism may be used by the nodes202-210 in the shared ledger system 200 to decide whether it isappropriate to make received changes to the distributed ledger 212.

Each node in the system therefore may have its own copy of thedistributed ledger 212, which may be identical to every other copy ofthe distributed ledger 212 stored by the other nodes. The shared ledgersystem 200 may be more robust than a central authority database systembecause of the shared ledger's decentralized nature. As such, there maybe no single point of failure on the shared ledger system 200, as theremay be in a centralized system.

Exemplary Transaction Flow & Block Propagation Flow

FIG. 3 depicts exemplary validating network nodes and an exemplarytransaction flow 300 on a shared ledger network for resolvingtransactions in accordance with one aspect of the present disclosure.FIG. 3 includes two time frames 320 and 322 represented by the left andright sides of the dotted line, respectively, Node A 302 and Node B 304,a set of transactions 308A-308D, a set of blocks of transactions309A-309D, a distributed ledger 310, and a blockchain 318.

The block propagation flow 300 may begin with Node A 302 receivingtransaction 306 at time 320. When Node A 302 confirms that transaction306 is valid, Node A 302 may add the transaction to a newly generatedblock 308. As part of adding the transaction 306 to block 308, Node A302 may solve a cryptographic puzzle and include the solution in thenewly generated block 308 as proof of the work done to generate theblock 308. Alternatively, a proof of stake algorithm may be used togenerate the block 308, whereby Node A 302 “stakes” an amount of adigital token used on the network, however, the network itself maydetermine the node that will mint the new block. In other embodiments,the transaction 306 may be added to a pool of transactions until asufficient number of transactions in the pool exist to form a block.Node A 302 may transmit the newly created block 308 to the network at312. Before or after propagating the block 308, Node A 302 may add theblock 308 to its copy of the blockchain 318.

The transactions 309A-309D may include updates to a state database 316.The state database 316 may contain current values of variables createdby smart contracts deployed on the blockchain 318. Validated blocks,such as block 308, may include transactions affecting state variables instate database 316. At time 322, Node B 304 may receive the newlycreated block 308 via the network at 312. Node B 304 may verify that theblock of transactions 308 is valid by checking the solution to thecryptographic puzzle provided in the block 308. If the solution isaccurate, then Node B 304 may add the block 308 to its blockchain 318and make any updates to the state database 316 as rejected by thetransactions in block 308. Node B 304 may then transmit the block 308 tothe rest of the network at 314.

Exemplary Node

FIG. 4 depicts exemplary components of a network node 400 on a sharedledger network for resolving subrogation claims, in accordance with oneaspect of the present disclosure. Node 400 is capable of performing thefunctionality disclosed herein. Node 400 may include at least oneprocessor 402, memory 404, a communication module 406, a set ofapplications 408, external ports 410, user interface 412, a blockchainmanager 414, smart contracts 416, operating system 418, a display screen420, and input/output components 422. In some embodiments, the node 400may generate a new block of transactions, or may broadcast transactionsto other network nodes by using the blockchain manager 414.

Similarly, the node 400 may use the blockchain manager 414 inconjunction with the smart contracts 416 stored in memory 404 to executethe functionality disclosed herein. The memory 404 may further includechain data 424 including, for example, a state database of theblockchain for storing state of smart contracts deployed thereon.

In other embodiments, the smart contracts 416 may operate independent ofthe blockchain manager 414, or other applications. In some embodiments,node 400 may not have a blockchain manager 414, or smart contracts 416stored at the node. In some embodiments, the node 400 may haveadditional or less components than what is described. The components ofthe node 400 are described in more detail below.

The node 400, as part of a decentralized ledger system 112, or anotherdecentralized or centralized network, may be used as part of systemsthat interact with and/or manipulate data and transactions associatedwith the automotive claims process, the vehicle loss history process,and/or the vehicle identification number lifecycle process.

Exemplary Smart Contract State

FIG. 5 depicts an exemplary smart contract state 500 in a shared ledgernetwork for resolving subrogation claims in accordance with one aspectof the present disclosure. A smart contract may be deployed by anyparticipant in the subrogation blockchain network (e.g., a subrogationclaimant) to establish a contract state 506 for a particular subrogationclaim. The deployed smart contract may expose methods and data to otherparticipants in the subrogation blockchain network. Some of the data inthe smart contract state may be private data that may only be altered bycalling a method of the smart contract, or only altered by authorizedblockchain participants.

One way of altering the smart contract state 506 may be to broadcast atransaction to the subrogation blockchain 502. If the broadcasttransaction satisfies consensus rules, network validators may includethe transaction in a block 504. Inclusion in the blockchain 502 of atransaction sending data to the smart contract may cause validatingnodes to update a state database, thus allowing network participantsaccess to a rich state mechanism to manage the subrogation process, andultimately to resolve the subrogation claim.

Subrogation smart contract state 506 may include pieces of data toidentify and track the subrogation claim. For example, a contract ownermay select a unique ID for the subrogation claim such that subsequenttransactions and data sent to the smart contract may identify thecontract by ID number. The contract owner may also specify an identityof the subrogation claimant and defendant. In at least oneimplementation, the subrogation claimant and defendant may be identifiedby cryptographic public keys assigned to the respective entities.Subsequent data sent to the smart contract may include a message signedby private keys corresponding to the public keys identifying thesubrogation claimant and defendant in the smart contract, thus providingcryptographic proof that the transaction was originated by one of theparties to the dispute. The private and public keys may be managedsolely by the parties to minimize the attack surface for any attackersthat might attempt to forge a transaction (e.g., the parties generatepublic/private cryptographic key pairs offline and only provide thepublic key to other network participants). A party's private keys may begenerated according to a securely stored seed value (e.g., on a piece ofphysical paper or multiple copies of a piece of paper) such that theprivate keys may be recovered in the case of a data loss.

The smart contract state 506 may further include information regardingthe basis of the subrogation claim, such as the policy holder and adescription of the damages suffered (e.g., date, time, place, etc.). Asubrogation blockchain network participant may monitor the blockchain502 for any subrogation claims that identify the participant as asubrogation defendant. As such, it is not necessary for a subrogationclaimant to notify a subrogation defendant “off-chain” of the existenceof the claim. A subrogation claimant may additionally make such anotification to the subrogation defendant as a redundant communication.

Resolving a subrogation claim may require assembling evidence of thedamages suffered by a policy holder. The evidence may take the form ofexpert or witness statements (e.g., a statement from a treating doctorthat an injury and/or medical care was the result of a collision,statement of a witness to a collision tending to establish the fault ofthe subrogation defendant's insured vehicle driver, etc.). The evidencemay also take the form of documentary evidence (e.g., report from anauthorized automotive repair services provider of damage to a vehicle asthe result of a collision, a repair estimate, a repair bill for repairservices rendered, a certification from a government entity that avehicle involved in a collision had a valid registration, etc.).Similarly, evidence may be supplied by oracles, such as by the oracles148 discussed in FIG. 1 .

As evidence regarding the subrogation claim may be collected from thevarious entities involved (medical, auto repair, governmental, etc.),these entities may broadcast transactions to the blockchain 502 toreflect the status of the loss and to provide the evidence therefor toother network participants, specifically the subrogation claimant anddefendant. For example, a doctor who treated a patient for injuriessustained in a collision may broadcast a transaction sending data to thesmart contract to connect the patient's injuries to the collision. Theevidence may take the form of a cryptographically signed statement fromthe doctor attesting to the injuries. The evidence may take the form ofa digitized X-ray or other medical record tending to prove the existenceof an injury. The evidence may further take the form of medical billsissued by the hospital for services rendered for the injuries.

Like the subrogation claimant and defendant, a doctor or hospital mayown a private cryptographic key that corresponds to a publiccryptographic key known to belong to the hospital or doctor by the othernetwork participants. By signing any submitted evidence with the privatecryptographic key, the hospital or doctor may provide cryptographicproof of identity to the subrogation defendant that the evidence wastruly submitted by the doctor or hospital. A subrogation defendant maychoose to rely solely on the cryptographic proof offered by thedoctor/hospital without separately contacting the doctor/hospital toverify the data. In this way, the blockchain 502 may reduce time andcost associated with resolving a subrogation claim.

The subrogation defendant may also submit comments in response to theevidence by broadcasting transactions to the blockchain 502.Additionally, the subrogation claimant may submit comments in responseto the subrogation defendant's comments by broadcasting transactions tothe blockchain 502, and the subrogation defendant and claimant may havea discussion back and forth that is broadcasted to the blockchain 502.The comments that form the discussion back and forth may be stored aspart of the smart contract state data for recordkeeping purposes.

Another aspect of the subrogation smart contract state 506 associatedwith a subrogation claim may be the smart contract data. Smart contractdata may be thought of like the private and public data in an objectcreated according to an object-oriented programming paradigm in thatthey may be directly updated from outside the object, or they may beupdated only in limited ways, such as by calling a method of the smartcontract. In at least one implementation, smart contract data mayinclude an indication (e.g., a flag) as to whether the parties to thesubrogation claim accept evidence in the smart contract asrepresentative of the damages owed by the subrogation defendant. Theseflags may be set according to methods in the smart contract that mayrequire the caller to prove its identity. The method may only permit,for example, a subrogation defendant to set a flag indicating thesubrogation defendant's acceptance of a component of the damages of thesubrogation claim. For example, once sufficient evidence relating to thecost of a medical treatment has been included in the smart contract, asubrogation claimant may indicate its approval of the evidence bysetting a flag.

A subrogation defendant, upon review of the medical evidence, may chooseto either set its corresponding flag to indicate its acceptance of themedical evidence, or it may decline to do so if it disputes the veracityof the evidence. As such, the smart contract state may track the variouscomponents of the damages owed and may refine points of dispute for theparties to the subrogation claim. When all sources of evidence for thevalue of the subrogation claim have been approved by the subrogationclaimant and defendant, the value of the claim may have been determinedand agreed upon, and a subrogation defendant may mark the settlement asagreed by sending data to the smart contract. Additionally, thesubrogation defendant may mark the settlement as paid. In at least oneimplementation, the blockchain 502 may include a circulating tokenhaving value with which the subrogation defendant may pay thesubrogation claimant.

The smart contract data may also include an indication (e.g., a flag) asto whether each of the parties to the subrogation claim have provided anoffer or a counter-offer, and the corresponding amount of the offer orcounter-offer. These flags may be set according to methods in the smartcontract that may require the caller to prove its identity. The methodmay only permit, for example, the opposing party to set a flagindicating the opposing party's counter-offer and the amount of thecounter-offer. For example, when the subrogation defendant submits anoffer, only the subrogation claimant may set a flag indicating acounter-offer and the amount of the counter-offer. The offers andcounter-offers that represent the negotiation back and forth may bestored as part of the smart contract state data for recordkeepingpurposes.

FIG. 5 depicts an exemplary blockchain system 500 in accordance with oneaspect of the present disclosure. FIG. 5 includes a blockchain 502, ablock of transactions 504, a Merkle Tree 506, and a transaction 508. TheMerkle Tree may be the above-referenced Merkle Tree thatcryptographically links transactions together. In other embodiments, theblockchain system 500 may utilize a different method of organizingtransactions in a block. In some embodiments, the blockchain system 500may include a plurality of blocks connected together to form a chain ofblocks of transactions 502.

In some implementation, the block of transactions 504 may organize thetransactions it has received into a Merkle Tree to facilitate access tothe stored transactions. The transactions may be hashed using acryptographic hash algorithm, such as the algorithms discussed above,and the hash of each transaction may be stored in the tree. As the treeis constructed, the hash of each adjacent node at the same level may behashed together to create a new node that exists at a higher level inthe tree. Therefore, the root of the tree, or the node at the top of thetree, may be dependent upon the hash of each transaction stored below inthe tree. Each transaction may include a set of data. The set of datamay include identifying data for the transaction, and transaction dataidentifying the nature of the transaction and what the transactionentails (e.g., input and output addresses, a transaction value, adocument hash value, a timestamp, a transaction fee value, etc.).

In one implementation, documents stored “on” a blockchain may bedocuments that have been hashed according to a cryptographic hashingalgorithm (e.g., SHA-256), and the resulting output hash may be includedin a transaction in a block that has been accepted by the network nodesas satisfying the consensus rules of the blockchain. As such, thedocuments may be later verified or validated by comparing the hash ofthe documents to the hash stored on the blockchain. For example, if aset of documents results in a SHA-256 hash that was recorded on ablockchain on a certain date, then the blockchain may providecryptographic proof that the documents existed as of that date.

One way of storing a document on a blockchain may be to broadcast atransaction including a hash of the document to the network, which maybe included in a block if the transaction satisfies all of the consensusrules of the network. In some implementations, the blockchain may be apermissioned ledger, meaning only authorized network participants maybroadcast transactions. In other implementations, only some authorizednetwork participants may make certain transactions. For example, vehicletelematics data tending to show which vehicle was at fault in acollision may be uploaded by the vehicle to the blockchain 502contemporaneously with, or subsequent to, a collision. Only acryptographic hash of the data may be included in the blockchain 502such that the data may be verified using the blockchain even if it isobtained by a party off-chain.

Validating network nodes may verify that the signed transaction orsigned message was signed by the private cryptographic key correspondingto the published public cryptographic key owned by the authorizedvehicle manufacturer. In at least one implementation, a validproof-of-identity may be applied as a consensus rule by the blockchainnetwork. As such, any transaction attempting to add a new VIN number tothe blockchain without a cryptographic proof-of-identity matching anidentity authorized to add a new VIN number may be rejected by thenetwork as non-compliant with the consensus rule.

FIG. 6 depicts an exemplary transaction 600 representing an evidencetransaction generated by an evidence oracle associated with one aspectof the present disclosure. The evidence oracle may collect data on atraffic intersection 612. This intersection may be of interest toinsurers if it has a history for being a dangerous intersection wherecollisions frequently occur. The data may be packaged into atransaction, such as transaction 606. The evidence oracle may broadcasttransaction 606 to distributed ledger 602 to be included in a block,such as block 604. The transaction 606 may include data such as atransaction ID, an originator (identified by a cryptographicproof-of-identity, and/or a unique oracle ID), an evidence type, such asvideo and audio evidence, and a cryptographic hash of the evidence. Inanother implementation, the evidence may not be stored as acryptographic hash, but may be directly accessible in block 604 by anobserver or other network participant, such as the participants in asubrogation claim.

Evidence Oracles

FIG. 7 depicts an exemplary flow diagram 700 associated with one aspectof the present disclosure, and in particular, providing data relevant toaccidents and subrogation claims by interacting with a distributedledger. The computer-implemented method depicted in FIG. 7 is oneexemplary implementation, whereas alternative methods and systems arediscussed below. In some embodiments, the network of participants may bethe nodes described above, for example node 400 depicted in FIG. 4 . Theblockchain used by the participants may be the blockchain 200 depictedin FIG. 2 , whose operation is described above. The steps of thecomputer-implemented method 700 may be performed by the nodes in thenetwork of participants, such as the nodes described in FIGS. 1-4 . Inaddition, the method may interact with smart contracts stored in thedistributed ledger, such as the exemplary smart contract depicted inFIG. 5 . The method 700 may include additional, fewer, or alternativeactions, including those described elsewhere herein.

In one embodiment, a computer-implemented method for providing datarelevant to accidents and subrogation claims by interacting with adistributed ledger may be provided. The method may be executed by anoracle, such as the evidence oracles discussed with respect to FIGS. 1and 6 . The method may include (1) receiving (such as via wirelesscommunication or data transmission over one or more radio links orcommunication channels), at one or more processors (and/or associatedtransceivers), recorded data from one or more connected devices at ageographic location (block 702); (2) analyzing, at the one or moreprocessors, the recorded data, wherein analyzing the recorded data mayinclude determining that an accident has occurred involving one or morevehicles (block 704); (3) generating, at the one or more processors, atransaction including the data indicative of the accident based upon theanalysis (block 706); and/or (4) transmitting (such as via wirelesscommunication or data transmission over one or more radio links orcommunication channels), at the one or more processors (and/orassociated transceivers), the transaction to at least one otherparticipant in the distributed ledger network (block 708).

In some embodiments, the recorded data may be sensor data, audio data,video data, image data, vehicle or home telematics data, smart vehicledata, autonomous or semi-autonomous vehicle data, smart orinterconnected data, and combinations thereof. The recorded data may bedata that is input into the distributed ledger by oracles, such as bythe oracles discussed with respect to FIGS. 1 and 6 . The data may berecorded data that is recorded in real-time by the oracle as eventsoccur. Real-time data may be data that is representative of events asthey happen in “real-time,” that is where the interval between datacollection periods is exceedingly small, such as by the second, or bythe microsecond. Alternatively, for some data types, data collectedevery few seconds or minutes may be considered real-time data. This datamay be packaged into transactions that are broadcast to the distributedledger on a periodic basis, such as, for example, by the second, minute,hour, day, week, or month.

In some embodiments, the geographic location may be a trafficintersection, a road segment, a waterway, a bridge, or combinationsthereof. The geographic location may be a location that is known by aninsurance provider to be a dangerous location where accidents are knownto occur.

In some embodiments, determining an accident has occurred may includecomparing, at the one or more processors, the recorded data to abaseline data profile for a type of recorded data related to thereceived recorded data. The baseline data profile may depict what“normal” conditions are at the geographic location when no accidents areoccurring. For example, a picture of street with no cars on it may be anormal condition. Alternatively, a picture of two cars that crashed intoeach other may be an accident condition. The baseline profile may bebuilt by analyzing historical data for the geographic location, as wellas historical claims data provided by an insurance provider.

In some embodiments, generating the transaction, may includeidentifying, at the one or more processors, identity data for the one ormore connected devices; augmenting, at the one or more processors, thetransaction with the identity data for the one or more connecteddevices; generating, at the one or more processors, a cryptographicsignature based upon the transaction; and/or augmenting, at the one ormore processors, the transaction with the cryptographic signature. Theidentity data may be an identification number assigned to a connecteddevice at the device's creation, and/or an identity number assigned tothe device when it joined the distributed ledger network. In someembodiments, the identity data may be data for the vehicles involved inthe accident, such as an identifier for a vehicle created at thevehicle's creation, a number provided by an insurance provider for thevehicle, an insurance policy number, or combinations thereof.

Some embodiments of the method may include, adding, at the one or moreprocessors, the transaction to a block of transactions; solving, at theone or more processors, a cryptographic puzzle based upon the block oftransactions; adding, at the one or more processors, the solution to thecryptographic puzzle to the block of transactions; and/or transmitting,at the one or more processors, the block of transactions to at least oneother participant in the distributed ledger network. Similarly, in otherembodiments a proof of stake algorithm, such as the one discussed abovewith respect to FIG. 3 , may be used when constructing a block oftransactions.

In some embodiments of the method, an accident has not occurred, or itis unclear if an accident has occurred from the evidence available. Inthis situation, the method may include updating, at the one or moreprocessors, a database with the recorded data.

An alternative embodiment of the method may include receiving (such asvia wireless communication or data transmission over one or more radiolinks or communication channels), at one or more processors and/orassociated transceivers, a request for recorded data from at least oneother participant in the distributed ledger network; verifying, at theone or more processors, an access level for the at least one otherparticipant; analyzing, at the one or more processors, the request forrecorded data, wherein analyzing may include determining data relevantto the request; generating, at the one or more processors, a transactionincluding the data relevant to the request; and/or transmitting, at theone or more processors and/or associated transceivers, the transactionto the at least one other participant in the distributed ledger network.

In certain embodiments of the aforementioned method, the request forrecorded data may be triggered by a transaction on the distributedledger network related to a subrogation claim. For example, aparticipant in the network may monitor the network for transactionsrelated to a subrogation claim, and subsequently request relevantevidence data from any evidence nodes on the network.

In some embodiments of the method, verifying the access level for the atleast one participant requesting the recorded data may further includedetermining, at the one or more processors, an identity for the at leastone other participant; and/or checking, at the one or more processors,the identity against a database of access levels listing who may accessthe recorded data. Not all participants in the distributed ledgernetwork may have the necessary authority, legally or otherwise, to viewall other participants and data transacted on the network. In thismanner, the distributed ledger network may be a permissioned network.The nodes may have varying levels of authority allowing them to seeparticular data they have the right to access. These rights may betracked as part of a “white list,” or “access list,” that records whomhas access to what information. This list may be cross-referenced aspart of the verification process.

In some embodiments, analyzing the request for recorded data may furtherinclude determining, at the one or more processors, a type of datarequested; and/or determining, at the one or more processors, an amountof data requested. Oracles may submit different types of data, such asvideo, audio, environmental, etc., and the data type may need to bedetermined before answering a request.

In some embodiments, generating the transaction, may includeidentifying, at the one or more processors, identity data for one ormore connected devices that recorded the recorded data; augmenting, atthe one or more processors, the transaction with the identity data forthe one or more connected devices; generating, at the one or moreprocessors, a cryptographic signature based upon the transaction; and/oraugmenting, at the one or more processors, the transaction with thecryptographic signature. Transactions may be secured using cryptographichashes so that participants in the network may verify the provenance ofthe transaction.

The method may include adding, at the one or more processors, thetransaction to a block of transactions; solving, at the one or moreprocessors, a cryptographic puzzle based upon the block of transactions;adding, at the one or more processors, the solution to the cryptographicpuzzle to the block of transactions; and/or transmitting, at the one ormore processors and/or associated transceivers, the block oftransactions to at least one other participant in the distributed ledgernetwork. Similarly, in other embodiments, a proof of stake algorithm,such as the one discussed above with respect to FIG. 3 , may be usedwhen constructing a block of transactions.

In one alternative embodiment, a computer system for providing datarelevant to accidents and subrogation claims by interacting with adistributed ledger may be provided. The system may include a networkinterface configured to interface with a processor; one or more sensors;a memory configured to store non-transitory computer executableinstructions and configured to interface with the processor; and theprocessor may be configured to interface with the memory. The processormay be configured to execute the non-transitory computer executableinstructions to cause the processor to: (1) receive recorded data fromone or more connected devices at a geographic location; (2) analyze therecorded data, wherein analyzing the recorded data may includedetermining that an accident has occurred involving one or morevehicles; (3) generate a transaction including the data indicative ofthe accident based upon the analysis; and/or (4) transmit thetransaction to at least one other participant in the distributed ledgernetwork.

Recommended Subrogation Amounts

FIG. 8 depicts an exemplary flow diagram 800 associated with one aspectof the present disclosure, and in particular, generating suggestedsubrogation amounts using machine learning. The computer-implementedmethod depicted in FIG. 8 is one exemplary implementation, whereasalternative methods and systems are discussed below. In someembodiments, the network of participants may be the nodes describedabove, for example node 400 depicted in FIG. 4 . The blockchain used bythe participants may be the blockchain 200 depicted in FIG. 2 , whoseoperation is described above. The steps of the computer-implementedmethod 800 may be performed by the nodes in the network of participants,such as the nodes described in FIGS. 1-4 . In addition, the method mayinteract with smart contracts stored in the distributed ledger, such asthe exemplary smart contract depicted in FIG. 5 . The method 800 mayinclude additional, fewer, or alternative actions, including thosedescribed elsewhere herein.

Determining subrogation amounts may be a difficult and time consumingprocess. A software program that leverages machine learning may be ableto monitor transactions on a distributed ledger and suggest subrogationamounts related to subrogation claims that are being processed. Forexample, a computer-implemented method for generating suggestedsubrogation amounts using machine learning, including using deeplearning or reinforced learning techniques, may be provided. The methodmay include (1) monitoring, at one or more processors and/or associatedtransceivers, transactions on the distributed ledger (block 802); (2)identifying, at the one or more processors, a transaction related to asubrogation claim (block 804); (3) analyzing, at the one or moreprocessors, the transaction related to the subrogation claim (block806); (4) generating, at the one or more processors, a recommendedsubrogation resolution using a machine learning algorithm (block 808);and/or (5) transmitting, at the one or more processors and/or associatedtransceivers, a transaction including the recommended subrogationresolution to a smart contract stored on the distributed ledger (block810).

In some embodiments, monitoring transactions on the distributed ledgermay further include monitoring, at the one or more processors and/orassociated transceivers, a smart contract stored at an address on thedistributed ledger. The smart contract may be a subrogation smartcontract with a state, such as the smart contract state 506 depicted inFIG. 5 .

In some embodiments, identifying a transaction related to a subrogationclaim may further include identifying, at the one or more processors, asubrogation ID in a transaction; and/or validating, at the one or moreprocessors, the subrogation ID. The subrogation ID may be for theclaimant, a defendant, or even for the address where a smart contract islocated.

In some embodiments, analyzing the transaction related to thesubrogation claim may further include analyzing, at the one or moreprocessors, damages data contained in the transaction; and/or analyzing,at the one or more processors, services rendered data contained in thetransaction. The damages data may be collected by oracles, such as theoracles depicted in FIGS. 1 and 6 . The services rendered data may bederived from transactions on the distributed ledger, or retrieved fromaddresses on the distributed ledger.

In some embodiments, generating a recommended subrogation resolutionusing a machine learning algorithm, may further include executing, atthe one or more processors, a machine learning algorithm on damages dataand services rendered data included in the transaction. The machinelearning algorithm may be trained on historical insurance claims data.This data may be used to help the algorithm “learn” optimal amounts forsubrogation claims based upon past filings. The machine learningalgorithm may be a supervised learning algorithm, employ decision trees,make use of an artificial neural network, make use of Bayesianstatistical analysis, deep learning and/or reinforced or reinforcementlearning algorithm, or combinations thereof.

In some embodiments, the machine learning algorithm may includecomparing, at the one or more processors, the damages data and servicesrendered data to a historical dataset for damages data and servicesrendered data; and/or identifying, at the one or more processors,similarities and differences between the datasets.

In some embodiments, generating a recommended subrogation resolutionusing a machine learning algorithm, may further include determining, atthe one or more processors, a subrogation amount for an at-faultinsurer, and a not-at-fault insurer.

Some embodiments of the method include adding, at the one or moreprocessors, the transaction to a block of transactions; solving, at theone or more processors, a cryptographic puzzle based upon the block oftransactions; adding, at the one or more processors, the solution to thecryptographic puzzle to the block of transactions; and/or transmitting,at the one or more processors, the block of transactions to at least oneother participant in the distributed ledger network. Similarly, in otherembodiments a proof of stake algorithm, such as the one discussed abovewith respect to FIG. 3 , may be used when constructing a block oftransactions.

An alternative embodiment of the method may include (1) receiving (suchas via wireless communication or data transmission over one or moreradio links or communication channels), at the one or more processorsand/or associated transceivers, a transaction related to a subrogationclaim; (2) analyzing, at the one or more processors, the transactionrelated to the subrogation claim; (3) generating, at the one or moreprocessors, a recommended subrogation resolution based upon the analysisof the transaction; and/or (4) transmitting, at the one or moreprocessors, a transaction including the recommended subrogationresolution to a smart contract stored on the distributed ledger.

Yet another alternative embodiment may be a computer system forinteracting with a distributed ledger. The system may include a networkinterface configured to interface with a processor; and a memoryconfigured to store non-transitory computer executable instructions andconfigured to interface with the processor. The processor may beconfigured to interface with the memory, wherein the processor may beconfigured to execute the non-transitory computer executableinstructions to cause the processor to: (1) monitor transactions on thedistributed ledger; (2) identify a transaction related to a subrogationclaim; (3) analyze the transaction related to the subrogation claim; (4)generate a recommended subrogation resolution using a machine learningalgorithm; and/or (5) transmit a transaction including the recommendedsubrogation resolution to a smart contract stored on the distributedledger.

Line Item Dispute Determination

FIG. 9 depicts an exemplary flow diagram 800 associated with one aspectof the present disclosure, and in particular, for providing a line itemdispute mechanism related to a subrogation claim. Thecomputer-implemented method depicted in FIG. 9 is one exemplaryimplementation, whereas alternative methods and systems are discussedbelow. In some embodiments, the network of participants may be the nodesdescribed above, for example node 400 depicted in FIG. 4 . Theblockchain used by the participants may be the blockchain 200 depictedin FIG. 2 , whose operation is described above. The steps of thecomputer-implemented method 800 may be performed by the nodes in thenetwork of participants, such as the nodes described in FIGS. 1-4 . Inaddition, the method may interact with smart contracts stored in thedistributed ledger, such as the example smart contract depicted in FIG.5 . The method 800 may include additional, fewer, or alternativeactions, including those described elsewhere herein.

Line item disputes may be common in the insurance claim process, and inparticular, the subrogation claim process. Line items may be entries ona bill for services rendered related to repairs performed as a result ofa collision, healthcare received as a result of a collision, orcombinations thereof.

An exemplary computer-implemented method for providing a line itemdispute mechanism related to a subrogation claim may be provided. Themethod may include (1) receiving (such as via wireless communication ordata transmission over one or more radio links or communicationchannels), at one or more processors and/or associated transceivers, atransaction from at least one other participant in the distributedledger network (block 902); (2) analyzing, at the one or moreprocessors, the transaction to determine a set of line items related toa subrogation claim (block 904); (3) comparing, at the one or moreprocessors, the set of line items to a baseline dataset (block 906); (4)generating, at the one or more processors, a transaction including adisputed line items dataset (block 908); and/or (5) transmitting, at theone or more processors and/or associated transceivers, the transactionto a smart contract stored on the distributed ledger (block 910).

In some embodiments, the transaction may include a transaction ID, asubrogation contract ID, an originator, a damages dataset, and/or aservices rendered dataset. Additional information may be included on thevehicles involved in the collision (including smart or autonomousvehicles), insurance providers for the vehicles, as well as any otherservice providers relevant to the collision. Similarly, information onthe drivers of the vehicles may also be included.

In some embodiments of the method, receiving (such as via wirelesscommunication or data transmission over one or more radio links orcommunication channels) the transaction, may further include verifying,at the one or more processors, an identifier for the at least one otherparticipant. The identifier may be assigned to the participant when theyjoin the distributed ledger network, may be an address where theparticipant holds funds on the network, and/or may be an identifiergenerated off the network by an issuing authority, such as a governmentagency, or private business.

In some embodiments, analyzing the transaction may further includedetermining, at the one or more processors, damages data and servicesrendered data included in the transaction. Determining this data mayinclude identifying the type of data included in the transaction, aswell as the amount of data included in the transaction.

In some embodiments, the baseline dataset is a dataset containingsuggested ranges for damages amounts and costs for services rendered.These suggested ranges may be maintained by a participant in thedistributed ledger network, such as an insurance provider or arbitrator.The suggested ranges may also be determined dynamically by analyzinghistorical claims data.

In some embodiments, comparing the set of line items to a baselinedataset may further include identifying, at the one or more processors,differences between amounts for the set of line items and amounts forthe baseline dataset. The difference may be an amount overpaid, asdetermined by the counter party, or by a party in the subrogationprocess as determined by the party subject to the subrogation.

In some embodiments of the method, generating the transaction includingthe disputed line items dataset, may further include generating, at theone or more processors, the disputed line items dataset based upon thecomparison of the set of line items to the baseline set.

Some embodiments of the method may include (1) receiving (such as viawireless communication or data transmission over one or more radio linksor communication channels), at the one or more processors and/orassociated transceivers, a response transaction related to the disputedline items; (2) analyzing, at the one or more processors, the responsetransaction; and/or (3) transmitting, at the one or more processorsand/or associated transceivers, an updated disputed line itemstransaction to at least one other participant. The response transactionmay be the result of the counterparty to the subrogation objecting to adisputed line item, providing evidence of the veracity of the disputedline item, or combinations thereof. Based upon this disputed line items,and the response, an updated line item transaction may be communicatedto participants in the network. Eventually, after a number oftransactions, there may be no more line items that are under dispute.

Another embodiment may involve a computer system for providing a lineitem dispute mechanism related to a subrogation claim. The system mayinclude a network interface configured to interface with a processor; amemory configured to store non-transitory computer executableinstructions and configured to interface with the processor; and theprocessor may be configured to interface with the memory. The processormay be configured to execute the non-transitory computer executableinstructions to cause the processor to: (1) receive a transaction fromat least one other participant in the distributed ledger network; (2)analyze the transaction to determine a set of line items related to asubrogation claim; (3) compare the set of line items to a baselinedataset; (4) generate a transaction including a disputed line itemsdataset; and/or (5) transmit the transaction to a smart contract storedon the distributed ledger.

In one alternative embodiment, a computer-implemented method forinteracting with a distributed ledger maintained by a plurality ofparticipants may be provided. The method may include (1) receiving, atone or more processors and/or associated transceivers, a disputed lineitem transaction from at least one other participant in the distributedledger network; (2) analyzing, at the one or more processors, thedisputed line item transaction; (3) generating, at the one or moreprocessors, a confirmation transaction including a consent datasetrelated to the disputed line item or items (block 906); and/or (4)transmitting, at the one or more processors and/or associatedtransceivers, the confirmation transaction to a smart contract stored onthe distributed ledger.

In some embodiments, the disputed line item transaction may include atransaction ID, a subrogation contract ID, an originator, a damagesdataset, and/or a services rendered dataset. In other embodiments,analyzing the disputed line item transaction, further may includedetermining, at the one or more processors, damages data and servicesrendered data included in the disputed line item transaction.

In some embodiments, generating the confirmation transaction includingthe consent dataset related to the disputed line items, further mayinclude generating, at the one or more processors, the consent datasetbased upon the comparison of the damages data and services rendered dataincluded in the disputed line item transaction to an acceptable consentdataset.

One alternative embodiment of the method may include (1) receiving (suchas via wireless communication or data transmission over one or moreradio links or communication channels), at one or more processors and/orassociated transceivers, a disputed line item transaction from at leastone other participant in the distributed ledger network; (2) analyzing,at the one or more processors, the disputed line item transaction; (3)generating, at the one or more processors, a confirmation transactionincluding a consent dataset related to the disputed line item or items;and/or (4) transmitting (such as via wireless communication or datatransmission over one or more radio links or communication channels), atthe one or more processors and/or associated transceivers, thetransaction to a smart contract stored on the distributed ledger. Theconfirmation transaction may be used by other parties involved in thesubrogation process to verify agreement on line items between theparties involved.

Vehicle Subrogation Claim Creation

FIG. 10 depicts an exemplary flow diagram 1000 associated with oneaspect of the present disclosure, and in particular, interacting with adistributed ledger to create subrogation claims related to a vehicleaccident. The computer-implemented method depicted in FIG. 10 is oneexemplary implementation, whereas alternative methods and systems arediscussed below. In some embodiments, the network of participants may bethe nodes described above, for example node 400 depicted in FIG. 4 . Theblockchain used by the participants may be the blockchain 200 depictedin FIG. 2 , whose operation is described above. The steps of thecomputer-implemented method 1000 may be performed by the nodes in thenetwork of participants, such as the nodes described in FIGS. 1-4 . Inaddition, the method may interact with smart contracts stored in thedistributed ledger, such as the exemplary smart contract depicted inFIG. 5 . The method 1000 may include additional, fewer, or alternativeactions, including those described elsewhere herein.

Vehicles today may have a plurality of computers contained within them.These computers monitor the status of the vehicle, as well as travelinformation pertaining to the vehicle. Accordingly, when a vehicle isinvolved in an accident, the computers in the vehicle may collect datarelated to the accident, as well as have data that may be relevant priorto when the accident occurred (such as telematics data (such as speed,location, GPS, cornering, braking, acceleration, and other types ofvehicle telematics data); image data; sensor data; audio data;autonomous or semi-autonomous vehicle system control decision,operational, or other data; etc.). This data may be collected from theparts or sensors of the vehicle. In some cases, due to the nature of thedata it may be obvious that the vehicle driver is not at fault. Forexample, if the vehicle is hit in the rear, the vehicle driver maylikely not be solely liable for any damages to the vehicle. As such, itmay be more efficient for the vehicle itself to create a subrogationclaim based off the data and transmit that claim as a transaction on adistributed ledger. If no distributed ledger exists, or the vehicle isnot connected to one, the vehicle may create a distributed ledger, orjoin a distributed ledger.

An exemplary computer-implemented method for interacting with adistributed ledger maintained by a plurality of participants may beprovided. The method may include (1) receiving (such as via wirelesscommunication or data transmission over one or more radio links orcommunication channels), at one or more processors and/or associatedtransceivers, sensor data indicative of a vehicle accident (block 1002);(2) analyzing, at the one or more processors, a set of components toassess potential damage (block 1004); (3) generating, at the one or moreprocessors, a damages dataset based upon the analysis (block 1006); (4)generating, at the one or more processors, a transaction including anidentifier for a vehicle involved in the vehicle accident and thedamages dataset (block 1008); and/or (5) transmitting, at the one ormore processors and/or transceivers, the transaction to at least oneother participant (block 1010).

In some embodiments, the sensor data may be received from the vehicle(which may be a smart, autonomous, or semi-autonomous vehicle) involvedin the vehicle accident. The sensor data may include image, telematics,audio, and/or other sensor data. For instance, the sensor data mayinclude operational data or control decision data associated with one ormore autonomous or semi-autonomous vehicle systems or features.Additionally or alternatively, the sensor data may be telematics orvehicle data indicative of braking, cornering, location, acceleration,direction, GPS location or speed, speed, position, orientation,location, impact, or combinations thereof. If the vehicle is capable ofvehicle-to-vehicle communication (V2V), the vehicle data may conform tocertain standards, such as the SAE J2735 Dedicated Short RangeCommunications Message Set. In some cases, the vehicle data may beretrieved from a cloud storage solution that receives vehicle datauploads for the vehicle as the vehicle is under operation.

In some embodiments, analyzing the set of components may further includecollecting, at the one or more processors, accident data from the set ofcomponents. The set of components of the vehicle may include the engine,the brakes, the tires, the air bags, the steering wheel, the suspensionsystem, the windows and windshields, the front and/or rear bumpers, theexhaust, the ignition, any telecommunications system the vehicle mayhave, as well as any other parts and/or smart parts or smart componentsthat comprise the vehicle.

In some embodiments of the method, analyzing the set of components mayfurther include (1) determining, at the one or more processors, asubrogation claim related to the vehicle accident; (2) generating, atthe one or more processors, a smart contract related to the subrogationclaim; and/or (3) deploying, at the one or more processors, the smartcontract to the distributed ledger. Determining the subrogation claimmay include analyzing the damage level done to any components in the setof components that were damaged in the accident. This information mayevidence, such as in the case of a “fender bender” where the vehicle isstruck from the rear, that the vehicle driver is not, or may not be,entirely at fault. As such, the smart contract may include thisinformation when determining the subrogation claim. The smart contractmay be deployed as part of a transaction broadcast to the distributedledger participants.

In some embodiments, generating the damages dataset may further includedetermining, at the one or more processors, a component status for eachmember of the set of components. As discussed above, the componentstatus may be data indicative of the components damage caused by theaccident.

In some cases, the damages dataset may include an insurance provider forthe vehicle involved in the vehicle accident. The insurance providerinformation may already be stored on the distributed ledger in atransaction related to the vehicle, or in other cases, the vehicleitself may store information on the insurance provider that insures thevehicle. Similarly, information on the drivers that operate the vehiclemay also be included in damages datasets.

In some embodiments, generating the transaction may further include (1)augmenting, at the one or more processors, the transaction with identitydata for the vehicle; (2) generating, at the one or more processors, acryptographic signature based upon the transaction; and/or (3)augmenting, at the one or more processors, the transaction with thecryptographic signature. Transactions may be secured using cryptographichashes so that participants in the network may verify the provenance ofthe transaction.

In some embodiments, transmitting the transaction to at least one otherparticipant, may further include transmitting, at the one or moreprocessors and/or associated transceivers, the transaction to a smartcontract stored on the distributed ledger. This smart contract may beowned by an insurance provider for the vehicle, or may be owned by thevehicle, or owned by the vehicle's owner.

In one alternative embodiment, a computer-implemented method forinteracting with a distributed ledger maintained by a plurality ofparticipants may be provided. The method may include (1) receiving (suchas via wireless communication or data transmission over one or moreradio links or communication channels), at one or more processors and/orassociated transceivers, sensor data indicative of a vehicle accident(the sensor data may include image data; audio data; autonomous orsemi-autonomous system or feature data (such as autonomous systemoperational or control decision data); telematics data; and/or othervehicle or mobile device sensor data); (2) analyzing, at the one or moreprocessors, a set of components, which may include smart or othercomponents, for a vehicle involved in the vehicle accident to assesspotential damage; (3) generating, at the one or more processors, adamages dataset based upon the analysis; (4) creating, at the one ormore processors, a distributed ledger for the vehicle involved in thevehicle accident; (5) deploying, at the one or more processors, a smartcontract including the damages dataset and an identifier for the vehicleinvolved in the vehicle accident on the distributed ledger; (6)generating, at the one or more processors, a transaction including theidentifier for a vehicle involved in the vehicle accident and thedamages dataset; and/or (7) transmitting, at the one or more processorsand/or associated transceivers, the transaction to at least one otherparticipant in the distributed ledger.

In another embodiment, a computer system for interacting with adistributed ledger may be provided. The system may include: a networkinterface configured to interface with a processor; a memory configuredto store non-transitory computer executable instructions and configuredto interface with the processor (and/or an associated transceiver); andthe processor may be configured to interface with the memory. Theprocessor may be configured to execute the non-transitory computerexecutable instructions to cause the processor to: (1) receive sensordata (including image or autonomous system data, or other types ofsensor data discussed elsewhere herein) indicative of a vehicleaccident; (2) analyze a set of components, which may include smart orother components, to assess potential damage; (3) generate a damagesdataset based upon the analysis; (4) generate a transaction including anidentifier for a vehicle involved in the vehicle accident and thedamages dataset; and/or (5) transmit the transaction to at least oneother participant.

Exemplary Data Sources

FIG. 11 depicts exemplary data sources providing data to serverconfigured to create blockchains or new blocks of data for insuranceclaim blockchains 1100. A server 1102, such as an insurance providerremote server, may collect data, with customer permission or affirmativeconsent, from customer mobile devices 1104, smart vehicles 1106,autonomous or semi-autonomous vehicles 1108, vehicles of 3^(rd) parties1110 (such as surrounding vehicles that may or may not have beeninvolved in a vehicle collision), smart infrastructure 1112, smart orinterconnected homes 1114, 3^(rd) parties 1116 (such as police or policevehicles, government computers, medical providers, repair shops, etc.),wearable electronic devices, such as smart watches 1118, and aerial data(such as from satellites 1120, drones 1122 (including autonomous orsemi-autonomous drones), or airplanes 1124 (including autonomous orsemi-autonomous airplanes), and/or other computing devices or sensors.The data collected may also include image data, video data, audio orsound data, sensor data, vehicle telematics or usage data, hometelematics or usage data, vehicle operational data, and/or otherinformation, including that discussed elsewhere herein.

The data generated by, and/or collected from, vehicles and/or mobiledevices may include vehicle telematics data, such as GPS location,speed, acceleration, deceleration, cornering, braking, heading, route,and other types of telematics data. The telematics data may be updatedperiodically during vehicle usage, such as every 0.001 or 0.005 seconds.

All of the computing devices shown in FIG. 11 may be parties orassociated with parties having access to communication network and/or aninsurance claim-related blockchain. For instance, a smart or autonomousvehicle may sense via one or more vehicle-mounted processors or sensorsthat it has been involved in a vehicle collision. The vehicle controllermay then gather or generate vehicle sensor data, image data, audio data,and/or telematics data, and create a new insurance claim-relatedblockchain or a new block to add to an existing blockchain (which mayinclude links to and/or the sensor and/or telematics data itself), andtransmit the block or blockchain, and/or the sensor, image, audio,and/or telematics data, to a remote server to start the insurance claimhandling process and/or vehicle reconstruction/assignment of faultprocess.

Telematics data, as used herein, may include telematics data, and/orother types of data that have not been conventionally viewed as“telematics data.” The telematics data may be generated by, and/orcollected or received from, various sources, including those shown inFIG. 11 . For example, the data may include, indicate, and/or relate tovehicle (and/or mobile device) speed; acceleration; braking;deceleration; turning or cornering; time; GPS (Global PositioningSystem) or GPS-derived location, speed, acceleration, or brakinginformation; vehicle and/or vehicle equipment operation; externalconditions (e.g., road, weather, traffic, and/or constructionconditions); other vehicles or drivers in the vicinity of an accident;vehicle-to-vehicle (V2V) communications; vehicle-to-infrastructure orinfrastructure-to-vehicle communications; drone-to-vehicle orvehicle-to-drone communications; vehicle-to-home or home-to-vehiclecommunications; drone-to-home or drone-to-infrastructure, or vice versa,communications; other smart or interconnected device-to-devicecommunications; and/or image and/or audio information of the vehicleand/or insured driver before, during, and/or after an accident. The datamay include other types of data, including those discussed elsewhereherein. The data may be collected via wired or wireless communication.

The data may be generated by mobile devices (smart phones, cell phones,lap tops, tablets, phablets, PDAs (Personal Digital Assistants),computers, smart watches, pagers, hand-held mobile or portable computingdevices, smart glasses, smart electronic devices, wearable devices,smart contact lenses, and/or other computing devices); smart vehicles;dash or vehicle mounted systems or original telematics devices; publictransportation systems; smart street signs or traffic lights; smartinfrastructure, roads, or highway systems (including smartintersections, exit ramps, and/or toll booths); smart or interconnectedhomes; smart trains, buses, or planes (including those equipped withWi-Fi or hotspot functionality); smart train or bus stations; internetsites; aerial, drone, or satellite images; 3^(rd) party systems or data;nodes, relays, and/or other devices capable of wireless RF (RadioFrequency) communications; and/or other devices or systems that captureimage, audio, or other data and/or are configured for wired or wirelesscommunication.

In some embodiments, the data collected may also derive from police orfire departments, hospitals, and/or emergency responder communications;police reports; municipality information; and/or other data collectedfrom government agencies and officials. The data from different sourcesor feeds may be aggregated.

The data generated may be transmitted, via wired or wirelesscommunication, to a remote server, such as a remote server and/or otherprocessor(s) associated with an insurance provider. The remote serverand/or associated processors may build a database of the telematicsand/or other data, and/or otherwise store the data collected.

The remote server and/or associated processors may analyze the datacollected, and then perform certain actions and/or issue tailoredcommunications based upon the data, including the insurance-relatedactions or communications discussed elsewhere herein. The automaticgathering and collecting of data from several sources by the insuranceprovider, such as via wired or wireless communication, may lead toexpedited insurance-related activity, including the automaticidentification of insured events, and/or the automatic or semi-automaticprocessing or adjusting of insurance claims.

In one embodiment, telematics data may be collected by a mobile device(e.g., smart phone) application. A telematics application that collectstelematics data may ask an insured for permission to collect and senddata about driver behavior and/or vehicle usage (including autonomousvehicle usage) to a remote server associated with an insurance provider.In return, the insurance provider may provide incentives to the insured,such as lower premiums or rates, or discounts and rebates. Thetelematics application for the mobile device may be downloadable off ofthe internet, in one embodiment.

During use, the telematics application or mobile device may, when withinBluetooth communication range, establish a Bluetooth connection with thevehicle, a vehicle controller, vehicle-mounted sensors, and/or otherelectronic devices within the vehicle capable of data transmission viaBluetooth frequencies. The Bluetooth communication may allow telematicsdata generated or collected by the mobile device to be communicated tothe vehicle controller or control system, or conversely allow telematicsdata generated or collected by the vehicle controller (and/orvehicle-mounted sensors) to be communication to the mobile device. Afterwhich, the vehicle or mobile device may wirelessly transmit thetelematics data collected to a remote server, such as insurance providerremote server, for further analysis.

Cause of Accident and/or Fault Determination

The present embodiments may determine the cause of a vehicle accidentfrom analyzing the telematics, image, sensor, audio, and/or other datacollected. An accident may be determined to have been fully, primarily,or partially caused by a number of factors, such as weather conditions,road or traffic conditions, construction, human error, technology error,vehicle or vehicle equipment faulty operation, and/or other factors.

In one aspect, the present embodiments may determine who or what was atfault (either entirely or partially) of causing a vehicle collision oraccident. Mobile devices, smart vehicles, equipment and/or sensorsmounted on and/or within a vehicle (including autonomous orsemi-autonomous vehicle systems), and/or roadside or infrastructuresystems may detect certain indicia of fault (for either one or moredrivers, or one or more autonomous vehicles), or perhaps moreimportantly from the insured's perspective, a lack of fault. An insuredmay opt-in to an insurance program that allows an insurance provider tocollect telematics, image, sensor, audio, and/or other data, and analyzethat data for low risk driving and/or other behavior. The analysis ofthe data and/or low risk behavior identified may be applied and/or usedto lower insurance premiums or rates for the insured or autonomousvehicle, and/or provide insurance discounts, rebates, or rewards to theinsured or autonomous vehicle owner.

Telematics, sensor, audio, and image data and/or other types of datagenerated or collected by, for example, (i) a mobile device (smartphone, smart glasses, etc.), (ii) cameras mounted on the interior orexterior of an insured (or other) vehicle, (iii) sensors or camerasassociated with a roadside system, and/or (iv) other electronic systems,such as those mentioned above, and/or may be time-stamped. The data mayindicate that the driver was driving attentively before, during, and/orafter an accident. For instance, the data collected may indicate that adriver was driving alone and/or not talking on a smart phone or textingbefore, during, and/or after an accident. Responsible or normal drivingbehavior may be detected and/or rewarded by an insurance provider, suchas with lower rates or premiums, or with good driving discounts for theinsured.

Additionally or alternatively, video or audio equipment or sensors maycapture images or conversations illustrating that the driver was drivinglawfully and/or was generally in good physical condition and calm beforethe accident. Such information may indicate that the other driver ormotorist (for a two vehicle accident), or even vehicle in the case of anautonomous vehicle, may have been primarily at fault.

Conversely, an in-cabin camera or other device may capture images orvideo indicating that the driver (the insured) or another motorist(e.g., a driver uninsured by the insurance provider) involved in anaccident was distracted or drowsy before, during, and/or after anaccident. Likewise, erratic behavior or driving, and/or drug or alcoholuse by the driver or another motorist, may be detected from varioussources and sensors. Telematics data, such as data gathered from thevehicle and/or a mobile device, may also be used to determine thatbefore or during an accident, one of the drivers was exhibiting good orproper driving behavior, or alternatively was speeding; followinganother vehicle too closely; and/or had time to react and avoid theaccident.

In addition to human drivers, fault may be assigned to one or moreautonomous or semi-autonomous vehicle systems or technologies. Forinstance, fault may be assigned vehicle collision avoidancefunctionality, such that the insured's insurance premium or rate may notbe negatively impacted by faulty technology. The telematics, image,sensor, and/or other data collected may include video and/or audio data,and may indicate whether a vehicle, or certain vehicle equipment,operated as designed before, during, and/or after the accident. Thatdata may assist in reconstructing a sequence of events associated withan insured event (e.g., a vehicle collision).

For instance, the data gathered may relate to whether or not the vehiclesoftware or other collision avoidance functionality (or other autonomousfeatures) operated as it was intended or otherwise designed to. Also, asmart vehicle control system or mobile device may use G-force dataand/or acoustic information to determine certain events. The data mayfurther indicate whether or not (1) an air bag deployed; (2) the vehiclebrakes were engaged; and/or (3) vehicle safety equipment (lights,wipers, turn signals, etc.), and/or other vehicle systems operatedproperly, before, during, and/or after an accident.

Fault or blame, whole or partial, may further be assigned toenvironmental or other conditions. Weather, traffic, or road conditions;road construction; other accidents in the vicinity; and/or otherconditions before, during, and/or after a vehicle accident (and in thevicinity of the location of the accident) may be determined (fromanalysis of the telematics, sensor, image, audio, and/or other datacollected) to have contributed to causing the accident and/or insuredevent. A percentage of fault or blame may be assigned to each of thefactors that contributed to causing an accident, and/or the severitythereof.

A sliding deductible and/or rate may depend upon the percentage of faultassigned to the insured or an insured autonomous or semi-autonomousvehicle. The percent of fault may be determined to be 0% or 100%, forexample, which may impact an amount that is paid by the insuranceprovider for damages and/or an insurance claim.

Collision Reconstruction

The telematics, image, audio, sensor and/or other data gathered orcollected from the various sources (such as mobile devices; smartvehicles; sensors or cameras mounted about a insured vehicle or avehicle associated with another motorist; public transportation systemsor other road side cameras; drone, aerial, or satellite images; etc.)may facilitate recreating the series of events that led to an accidentor vehicle collision. The data gathered may be used by investigativeservices associated with an insurance provider to determine, for avehicle accident, (1) an accident cause and/or (2) lack of fault and/orfault (or a percentage thereof) that is assigned or attributed to eachof the drivers and/or vehicles (e.g., autonomous vehicles or autonomousor semi-autonomous vehicle systems) involved. The data gathered may alsobe used to identify a non-human cause of the accident, such as roadconstruction, or weather, traffic, and/or road conditions, or faultyautonomous technologies.

A. Time-Stamped Sequence of Events

The series or sequence of events may facilitate establishing that aninsured had no, or minimal, fault in causing the accident. Suchinformation may lead to lower premiums or rates for the insured, and/orno change in insurance premiums or rates for the insured, or an insuredvehicle, because of a vehicle accident for which the driver or vehiclewas not at fault (and/or not primarily at fault). Proper faultdetermination, or a percentage thereof, may allow multiple insuranceproviders to assign proper risk to each driver or vehicle involved in anaccident, and adjust their respective insurance premiums or ratesaccordingly such that good driving behavior or proper vehicle operationis not improperly penalized.

In one aspect, audio and/or video data may be recorded. Audio and videodata may capture time-stamped sound and images, respectively. Sound andvisual data may be associated with and/or indicate vehicle braking;vehicle speed; vehicle turning; turn signal, window wiper, head light,and/or brake light normal or faulty operation; windows breaking; airbags deploying; and/or whether the vehicle or vehicle equipment(including autonomous or semi-autonomous features) operated as designedfor each vehicle involved in an insured event or vehicle accident.

B. Virtual Accident Reconstruction

The telematics, sensor, image, audio, and/or other data gathered mayfacilitate accident reconstruction, and an accident scene or series ofevents may be recreated. Noted above, from the series of events leadingup to, during, and/or after the accident, fault (or a percentage offault) may be assigned to an insured or another motorist, and/or one ormore vehicles (such as autonomous vehicles). The data gathered may beviewed as accident forensic data, and/or may be applied to assign faultor blame to one or more drivers, one or more vehicles or vehiclesystems, and/or one or more external conditions.

For example, the data gathered may indicate weather, traffic, roadconstruction, and/or other conditions. The data gathered may facilitatescene reconstructions, such as graphic presentations on a display of avirtual map. The virtual map may include a location of an accident;areas of construction; areas of high or low traffic; and/or areas of badweather (rain, ice, snow, etc.).

The virtual map may indicate a route taken by a vehicle or multiplevehicles involved in an accident. A timeline of events, and/or movementof one or more vehicles, may be depicted via, or superimposed upon, thevirtual map. As a result, a graphical or virtual moving or animatedrepresentation of the events leading up to, during, and/or after theaccident may be generated.

The virtual representation of the vehicle accident may facilitate (i)fault, or percentage of fault, assignment to one or more drivers,vehicles, or vehicle systems; and/or (ii) blame, or percentage of blame,assignment to one or more external conditions, such as weather, traffic,and/or construction. The assignments of fault or blame, or lack thereof,may be applied to handling various insurance claims associated with thevehicle accident, such as claims submitted by an insured or othermotorists, or vehicle owner. The insured or vehicle owner may be insuredby an insurance provider, and the other motorists may be insured by thesame or another insurance provider. The assignments of fault or blame,or lack thereof, may lead to appropriate adjustments to the insurancepremiums or rates for the insured and/or the other motorists, and/orinsured vehicles, to reflect the cause or causes of the accidentdetermined from the data collected.

The virtual representation of the vehicle accident may account forseveral vehicles involved in the accident. The sequence of eventsleading up to and including the accident may include analysis of thetelematics, sensor, image, audio, and/or other data to determine orestimate what each of several vehicles and/or respective drivers did (ordid not) do prior to, during, and/or after the accident.

As examples, voice data from using a smart phone to place a telephonecall before or during an accident may indicate a distracted driver.Vehicle sensors may detect seat belt usage, such as seat belt usagebefore or during an accident, and/or the frequency or amount of seatbelt usage by a specific driver. The data may reveal the number ofchildren or other passengers in a vehicle before or during an accident.

Moreover, GPS (Global Positioning System) location and speed data fromseveral vehicles may be collected. Other vehicle data may also becollected, such as data indicating whether (i) turn signals were used;(ii) head lights were on; (iii) the gas or brake pedal for a vehicle waspressed or depressed; and/or (iv) a vehicle was accelerating,decelerating, braking, maneuvering, turning, in their respective lane,and/or changing lanes.

Infrastructure data, such as image, audio, traffic, construction,weather, and/or other data from public transportation systems or smarttraffic lights, may also be collected. Thus, for each vehicle accidentor insured event a unique combination of data may be gathered at theinsurance provider remote server and then analyzed to determine a mostlikely series of events leading up to the insured event.

Autonomous Automobile Functionality

As noted above, the present embodiments may relate to assessing andpricing insurance based upon autonomous (or semi-autonomous)functionality of a vehicle, and not the human driver. A smart orautonomous vehicle may maneuver itself without human intervention and/orinclude sensors, processors, computer instructions, and/or othercomponents that may perform or direct certain actions conventionallyperformed by a human driver.

An analysis of how artificial intelligence facilitates avoidingaccidents and/or mitigates the severity of accidents may be used tobuild a database and/or model of risk assessment. After which,automobile insurance risk and/or premiums (as well as insurancediscounts, rewards, and/or points) may be adjusted based upon autonomousor semi-autonomous vehicle functionality, such as by groups ofautonomous or semi-autonomous functionality or individual features. Inone aspect, an evaluation may be performed of how artificialintelligence, and the usage thereof, impacts automobile accidents and/orautomobile insurance claims.

The types of autonomous or semi-autonomous vehicle-related functionalityor technology that may be used with the present embodiments to replacehuman driver actions may include and/or be related to the followingtypes of functionality: (a) fully autonomous (driverless); (b) limiteddriver control; (c) vehicle-to-vehicle (V2V) wireless communication; (d)vehicle-to-infrastructure (and/or vice versa), and/or vehicle-to-device(such as mobile device or smart home controller) wireless communication;(e) automatic or semi-automatic steering; (f) automatic orsemi-automatic acceleration; (g) automatic or semi-automatic braking;(h) automatic or semi-automatic blind spot monitoring; (i) automatic orsemi-automatic collision warning; (j) adaptive cruise control; (k)automatic or semi-automatic parking/parking assistance; (1) automatic orsemi-automatic collision preparation (windows roll up, seat adjustsupright, brakes pre-charge, etc.); (m) driver acuity/alertnessmonitoring; (n) pedestrian detection; (o) autonomous or semi-autonomousbackup systems; (p) road mapping systems; (q) software security andanti-hacking measures; (r) theft prevention/automatic return; (s)automatic or semi-automatic driving without occupants; and/or otherfunctionality.

In one embodiment, telematics and vehicle-mounted sensor data, and otherdata from the sources mentioned herein, may be generated and collected.From which, performance of various autonomous or semi-autonomous vehiclefunctionality or systems may be determined before, during, and/or aftera vehicle collision. During accident reconstruction, assignment of apercentage of fault, or lack thereof, may be assigned to the autonomousor semi-autonomous functionality and systems mentioned herein in use, orsupposed to be automatically engaged, during a vehicle collision.

Additionally or alternatively, the adjustments to automobile insurancerates or premiums based upon the autonomous or semi-autonomousvehicle-related functionality or technology may take into account theimpact of such functionality or technology on the likelihood of avehicle accident or collision occurring. For instance, a processor mayanalyze historical accident information and/or test data involvingvehicles having autonomous or semi-autonomous functionality. Factorsthat may be analyzed and/or accounted for that are related to insurancerisk, accident information, or test data may include (1) point ofimpact; (2) type of road; (3) time of day (TOD); (4) weather conditions;(5) road construction; (6) type/length of trip; (7) vehicle style; (8)level of pedestrian traffic; (9) level of vehicle congestion; (10)atypical situations (such as manual traffic signaling); (11)availability of internet connection for the vehicle; and/or otherfactors. These types of factors may also be weighted according tohistorical accident information, predicted accidents, vehicle trends,test data, and/or other considerations.

In one aspect, the benefit of one or more autonomous or semi-autonomousfunctionalities or capabilities may be determined, weighted, and/orotherwise characterized. For instance, the benefit of certain autonomousor semi-autonomous functionality may be substantially greater in city orcongested traffic, as compared to open road or country driving traffic.Additionally or alternatively, certain autonomous or semi-autonomousfunctionality may only work effectively below a certain speed, i.e.,during city driving or driving in congestion. Other autonomous orsemi-autonomous functionality may operate more effectively on thehighway and away from city traffic, such as cruise control.

Further, individual autonomous or semi-autonomous functionality may beimpacted by weather, such as rain or snow, and/or time of day (day lightversus night). As an example, fully automatic or semi-automatic lanedetection warnings may be impacted by rain, snow, ice, and/or the amountof sunlight (all of which may impact the imaging or visibility of lanemarkings painted onto a road surface, and/or road markers or streetsigns).

Automobile insurance premiums, rates, discounts, rewards, refunds,points, etc. may be adjusted based upon the percentage of time orvehicle usage that the vehicle is the driver, i.e., the amount of time aspecific driver uses each type of autonomous (or even semi-autonomous)vehicle functionality. In other words, insurance premiums (includingusage-based insurance premiums or rates), discounts, rewards, etc. maybe adjusted based upon the percentage of vehicle usage during which theautonomous or semi-autonomous functionality is in use. For example,automobile insurance risk, premiums, discounts, etc. for an automobilehaving one or more autonomous or semi-autonomous functionalities may beadjusted and/or set based upon the percentage of vehicle usage that theone or more individual autonomous or semi-autonomous vehiclefunctionalities are in use, anticipated to be used or employed by thedriver, and/or otherwise operating.

Such usage information for a particular vehicle may be gathered overtime and/or via remote wireless communication with the vehicle. Oneembodiment may involve a processor on the vehicle, such as within avehicle control system or dashboard, monitoring in real-time whethervehicle autonomous or semi-autonomous functionality is currentlyoperating. Other types of monitoring may be remotely performed, such asvia wireless communication between the vehicle and a remote server orwireless communication between a vehicle-mounted dedicated device (thatis configured to gather autonomous or semi-autonomous functionalityusage information) and a remote server.

In one embodiment, if the vehicle is currently employing autonomous orsemi-autonomous functionality, the vehicle may send a Vehicle-to-Vehicle(V2V) wireless communication to a nearby vehicle also employing the sameor other type(s) of autonomous or semi-autonomous functionality. As anexample, the V2V wireless communication from the first vehicle to thesecond vehicle (following the first vehicle) may indicate that the firstvehicle is autonomously braking, and the degree to which the vehicle isautomatically braking and/or slowing down. In response, the secondvehicle may also automatically or autonomously brake as well, and thedegree of automatically braking or slowing down of the second vehiclemay be determined to match, or even exceed, that of the first vehicle.As a result, the second vehicle, traveling directly or indirectly,behind the first vehicle, may autonomously safely break in response tothe first vehicle autonomously breaking.

As another example, the V2V wireless communication from the firstvehicle to the second vehicle may indicate that the first vehicle isbeginning or about to change lanes or turn. In response, the secondvehicle may autonomously take appropriate action, such as automaticallyslow down, change lanes, turn, maneuver, etc. to avoid the firstvehicle.

As noted above, the present embodiments may include remotely monitoring,in real-time and/or via wireless communication, vehicle autonomous orsemi-autonomous functionality. From such remote monitoring, the presentembodiments may remotely determine that a vehicle accident has occurred.As a result, emergency responders may be informed of the vehicleaccident location via wireless communication, and/or quickly dispatchedto the accident scene.

The present embodiments may also include remotely monitoring, inreal-time or via wireless communication, that vehicle autonomous orsemi-autonomous functionality is, or is not, in use, and/or collectinformation regarding the amount of usage of the autonomous orsemi-autonomous functionality. Operational data and control decisiondata from autonomous or semi-autonomous systems may be collected,including data control decision data indicating what control decisionwere implemented or not implemented by various autonomous orsemi-autonomous systems or features. From such remote monitoring, aremote server may remotely send a wireless communication to the vehicleto prompt the human driver to engage one or more specific vehicleautonomous or semi-autonomous functionalities.

Another embodiment may enable a vehicle to wirelessly communicate with atraffic light, railroad crossing, toll both, marker, sign, or otherequipment along the side of a road or highway. As an example, a trafficlight may wirelessly indicate to the vehicle that the traffic light isabout to switch from green to yellow, or from yellow to red. In responseto such an indication remotely received from the traffic light, theautonomous or semi-autonomous vehicle may automatically start to brake,and/or present or issue a warning/alert to the human driver. Afterwhich, the vehicle may wirelessly communicate with the vehiclestraveling behind it that the traffic light is about to change, and/orthat the vehicle has started to brake or slow down such that thefollowing vehicles may also automatically brake or slow downaccordingly.

Insurance premiums, rates, ratings, discounts, rewards, special offers,points, programs, claims, claim amounts, etc. may be adjusted for, ormay otherwise take into account, the foregoing functionality and/or theother functionality described herein. For instance, insurance policiesmay be updated based upon autonomous or semi-autonomous vehiclefunctionality; V2V wireless communication-based autonomous orsemi-autonomous vehicle functionality; and/or vehicle-to-infrastructureor infrastructure-to-vehicle wireless communication-based autonomous orsemi-autonomous vehicle functionality.

Exemplary Computer-Implemented Methods

FIG. 12 depicts an exemplary computer-implemented method of handling aninsurance claim via a shared ledger. The method 1200 may include, viaone or more processors, servers, sensors, and/or associatedtransceivers: (1) receiving an electronic notification of a vehiclecollision (such as via wireless communication or data transmission overone or more radio links or communication channels) (block 1202) from oneor more sources. The electronic notification, in some embodiments, mayinclude sensor, image, audio, and/or telematics data. The electronicnotification, additionally or alternative, may include proof ofinsurance from another vehicle or driver involved in the collision(and/or an image thereof), and/or another driver's driver licenseinformation (and/or an image thereof), and/or the other vehicle'slicense plate information (and/or an image thereof).

The method 1200 may include (2) receiving (such as via wirelesscommunication or data transmission over one or more radio links orcommunication channels) from one or more sources (such as the sourcesdepicted in FIG. 11 ), gathering, or generating telematics, sensor,audio, and/or image data (such as vehicle-to-vehicle data; smart orautonomous vehicle image, audio, sensor, operational, control decision(e.g., data indicative of control decisions determined or implemented byautonomous or semi-autonomous vehicle systems or technologies),telematics, or other data; mobile device data; smart infrastructuredata; aerial data; vehicle telematics data; smart or interconnected homedata; etc.) (block 1204). The data received or collected may alsoinclude electronic proof of insurance from another vehicle or driverinvolved in the collision (and/or an image thereof), and/or anotherdriver's driver license information in electronic format (and/or animage or scan thereof), and/or the other vehicle's license plateinformation in electronic format (and/or an image or scan thereof).

The method 1200 may include (3) determining a percentage of fault of thevehicle collision for one or more vehicles (e.g., for an autonomous orsemi-autonomous vehicle, or an autonomous or semi-autonomous vehiclesystem or technology) and/or vehicle drivers based upon, at least inpart, processor analysis of the telematics, sensor, image, and/or audiodata collected (block 1206); and/or (4) creating a blockchain for thevehicle collision, or a new block for an existing blockchain, with oneor more links to, or otherwise means for accessing, the telematics,sensor, image, and/or audio data collected, and an indication of thepercentage of fault determined (block 1208) to facilitateblockchain-based claim handling. The method 1200 may further includegenerating or receiving (such as via wireless communication or datatransmission) (i) a new block including an electronic subrogation demand(block 1210); (ii) a new block including an indication of accepting ordisputing the subrogation demand from another insurance carrier; and/or(iii) a new block including an electronic arbitration demand (block1212), such as when the subrogation demand is disputed. The method mayinclude additional, less, or alternate actions, including thosediscussed elsewhere herein.

FIG. 13 depicts a computer-implemented method of handling an insuranceclaim via a shared ledger. The method 1300 may include, via one or moreprocessors, servers, sensors, and/or associated transceivers: (1)receiving (such as via wireless communication or data transmission overone or more radio links or communication channels) from one or moresources, gathering, or generating telematics, sensor, image, and/oraudio data (such as vehicle-to-vehicle data; smart or autonomous vehicleimage, audio, sensor, operational, control decision (e.g., dataindicative of control decisions determined or implemented by autonomousor semi-autonomous vehicle systems or technologies), telematics, orother data; mobile device data; smart infrastructure data; aerial data;vehicle telematics data; smart or interconnected home data; etc.) (block1302); (2) determining a vehicle collision occurred from processoranalysis of the telematics, sensor, image, and/or audio data (block1304); (3) determining a percentage of fault of the vehicle collisionfor one vehicles, vehicle systems (e.g., for an autonomous orsemi-autonomous vehicle), and/or vehicle drivers based upon, at least inpart, processor analysis of the telematics, sensor, image, and/or audiodata collected (block 1306); and/or (4) creating a blockchain for thevehicle collision, or a new block for an existing blockchain, with oneor more links to, or otherwise means for accessing, the telematics,sensor, image, and/or audio data collected, and an indication of thepercentage of fault(s) determined (block 1308) to facilitateblockchain-based claim handling. The method 1300 may further includegenerating or receiving (such as via wireless communication or datatransmission) (i) a new block including an electronic subrogation demand(block 1310); (ii) a new block including an indication of accepting ordisputing the subrogation demand from another insurance carrier; and/or(iii) a new block including an electronic arbitration demand (block1312), such as when the subrogation demand is disputed. The method mayinclude additional, less, or alternate actions, including thosediscussed elsewhere herein.

FIG. 14 depicts a computer-implemented method of handling an insuranceclaim via a shared ledger. The method 1400 may include, via one or moreprocessors, servers, sensors, and/or associated transceivers: (1)receiving (such as via wireless communication or data transmission overone or more radio links or communication channels) from one or moresources, gathering, or generating telematics, sensor, image, and/oraudio data (such as vehicle-to-vehicle data; smart or autonomous vehicleimage, audio, sensor, operational, control decision (e.g., dataindicative of control decisions determined or implemented by autonomousor semi-autonomous vehicle systems or technologies), telematics, orother data; mobile device data; smart infrastructure data; aerial data;vehicle telematics data; smart or interconnected home data; etc.) (block1402); (2) determining a vehicle collision occurred from processoranalysis of the telematics, sensor, image, and/or audio data (block1404); (3) determining an operational mode of the vehicle before,during, and/or after the vehicle collision, such as determining whetherthe vehicle (e.g., an autonomous vehicle), vehicle system (e.g., anautonomous vehicle system or technology), or human driver was driving orotherwise in control of the vehicle) (block 1406); (4) determining apercentage of fault of the vehicle collision for a vehicle (e.g., for anautonomous or semi-autonomous vehicle or system) or a vehicle driverbased upon, at least in part, processor analysis of the telematics,sensor, image, and/or audio data collected (block 1408); and/or (5)creating a blockchain for the vehicle collision, or a new block for anexisting blockchain, with one or more links to, or otherwise means foraccessing, the telematics, sensor, image, and/or audio data collected,an indication of the percentage of fault(s) determined, and anindication of whether the vehicle, vehicle system, or driver was incontrol of each vehicle (block 1410) to facilitate blockchain-basedclaim handling.

The method 1400 may further include generating or receiving (such as viawireless communication or data transmission) (i) a new block includingan electronic subrogation demand (block 1412); (ii) a new blockincluding an indication of accepting or disputing the subrogation demandfrom another insurance carrier; and/or (iii) a new block including anelectronic arbitration demand (block 1414), such as when the subrogationdemand is disputed. The method may include additional, less, oralternate actions, including those discussed elsewhere herein.

In one aspect, a computer-implemented method of handling an insuranceclaim via a shared ledger may be provided. The method may include, viaone or more local or remote processors, servers, sensors, and/orassociated transceivers: (1) receiving an electronic notification of avehicle collision (such as via wireless communication or datatransmission over one or more radio links or communication channels);(2) receiving (such as via wireless communication or data transmissionover one or more radio links or communication channels) from one or moresources, gathering, or generating telematics, sensor, image, and/oraudio data (such as vehicle-to-vehicle data; smart or autonomous vehicleimage, audio, sensor, operational, control decision (e.g., dataindicative of control decisions determined or implemented by autonomousor semi-autonomous vehicle systems or technologies), telematics, orother data; mobile device data; smart infrastructure data; aerial data;vehicle telematics data; smart or interconnected home data; etc.); (3)determining a percentage of fault of the vehicle collision for one ormore vehicles or vehicle systems (e.g., for an autonomous orsemi-autonomous vehicle or system), and/or vehicle drivers based upon,at least in part, processor analysis of the telematics, sensor, image,and/or audio data collected; and/or (4) creating a blockchain for thevehicle collision, or a new block for an existing blockchain, with oneor more links to or hashes of, or otherwise means for accessing, thesensor and/or image data collected, and an indication of the percentageof fault determined to facilitate blockchain-based claim handling. Themethod may include additional, less, or alternate actions, includingthose discussed elsewhere herein.

For instance, the method may include, via one or more processors,servers, sensors, and/or associated transceivers: receiving (such as viawireless communication or data transmission over one or more radio linksor communication channels) an electronic arbitration demand associatedwith the vehicle collision; and/or generating a recommendation basedupon, at least in part, processor analysis of, or processor comparisonof, the percentage of fault and the electronic arbitration demand. Themethod may also include generating a new block including therecommendation, or a link thereto or hash thereof; and/or adding the newblock to the blockchain.

In one embodiment, the telematics, sensor, image, and/or audio data maybe generated by smart infrastructure, or by a vehicle not involved inthe vehicle collision. The telematics, sensor, image, and/or audio datamay include telematics data collected by the vehicle, a mobile devicetraveling within the vehicle, or another vehicle in the vicinity of thevehicle collision.

Determining a percentage of fault of the vehicle collision for avehicle, vehicle system, or a vehicle driver based upon, at least inpart, processor analysis of the sensor and/or image data collected mayinclude inputting the sensor and/or image data collected into a machinelearning program trained to identify a percentage of fault for avehicle, vehicle system, or vehicle driver based upon sensor and/orimage data.

The electronic notification of a vehicle collision may be generated bythe vehicle from processor analysis of sensor data generated by one ormore vehicle-mounted sensors. The electronic notification of a vehiclecollision may be generated by the vehicle from processor analysis ofimage data generated by one or more vehicle-mounted sensors or cameras.

The electronic notification of a vehicle collision may be generated bythe vehicle from processor analysis of telematics data (e.g., location,acceleration, speed, cornering, and braking data from before, during,and after the vehicle collision) generated by one or morevehicle-mounted sensors. Additionally or alternatively, the electronicnotification of a vehicle collision may be generated by the vehicle fromprocessor analysis of sensor data generated by one or morevehicle-mounted sensors, and sensor or other data, such as telematics,image, or audio data, received from the vehicle and/or one or morenearby vehicles in the vicinity of the vehicle collision location.

The method may include, via one or more processors, servers,transceivers, and/or sensors: receiving (such as via wirelesscommunication or data transmission over one or more radio links orcommunication channels) an electronic subrogation demand associated withthe vehicle collision; and/or generating an electronic recommendationbased upon, at least in part, processor analysis of, or processorcomparison of, the percentage of fault and the electronic subrogationdemand. The method may include generating a new block including theelectronic recommendation, or an indication thereof, or a link theretoor hash thereof; and/or adding the new block to the blockchain.

The method may include, via one or more processors, servers, sensors,and/or associated transceivers: generating an electronic subrogationdemand based upon, at least in part, processor analysis of thetelematics, sensor, image, and/or audio data collected and/or thepercentage of fault(s) determined for one or more vehicles, vehiclesystems, and/or vehicle drivers (such as via artificial intelligence ormachine learning techniques); and/or generating a block to add to theblockchain that includes a link to, hash of, or an indication of theelectronic subrogation demand. The electronic subrogation demand mayinclude one or more line items directed to medical expenses, vehiclerepair costs, vehicle towing services, and/or rental vehicle expenses.

The method may include, via one or more processors, servers, sensors,and/or associated transceivers: (1) receiving (such as via wirelesscommunication or data transmission over one or more radio links orcommunication channels) or retrieving an electronic subrogation demandassociated with the vehicle collision; (2) analyzing the electronicsubrogation demand, the telematics, sensor, image, and/or audio datacollected, and/or the percentage of fault(s) determined for one or morevehicles, vehicle systems, and/or vehicle drivers (such as by usingartificial intelligence and/or machine learning techniques); (3)generating a recommendation to accept or dispute the electronicsubrogation demand; and/or (4) generating a new block including a linkto, or an indication of, the recommendation to add to the blockchain.

The method may include, via one or more processors, servers, sensors,and/or associated transceivers: generating an electronic arbitrationdemand based upon, at least in part, the telematics, sensor, image,and/or audio data collected and/or the percentage of fault(s) determinedfor one or more vehicles, vehicle systems, and/or vehicle drivers;and/or generating a block to add to the blockchain that includes a linkto, hash of, or an indication of, the electronic arbitration demand.

In another aspect, a computer-implemented method of handling aninsurance claim via a shared ledger may be provided. The method mayinclude, via one or more local or remote processors, servers, sensors,and/or associated transceivers: (1) receiving (such as via wirelesscommunication or data transmission over one or more radio links orcommunication channels), gathering, or generating telematics, sensor,image, and/or audio data (such as vehicle-to-vehicle data; smart orautonomous vehicle image, audio, sensor, operational, control decision(e.g., data indicative of control decisions determined or implemented byautonomous or semi-autonomous vehicle systems or technologies),telematics, or other data; mobile device data; smart infrastructuredata; aerial data; vehicle telematics data; smart or interconnected homedata; etc.); (2) determining that a vehicle collision (such as viawireless communication or data transmission over one or more radio linksor communication channels) occurred based upon, at least in part,processor analysis of the telematics, sensor, image, and/or audio datacollected; (3) determining a percentage of fault of the vehiclecollision for one or more vehicles, vehicle systems, or vehicle driversbased upon, at least in part, processor analysis of the telematics,sensor, image, and/or audio data collected, received, or generated;and/or (4) creating a blockchain for the vehicle collision, or a newblock for an existing blockchain, with the telematics, sensor, image,and/or audio data, or one or more links to or hashes of the telematics,sensor, image, and/or audio data collected, and an indication of thepercentage of fault(s) determined to facilitate blockchain-based claimhandling. The method may include additional, less, or alternate actions,including those discussed elsewhere herein.

For instance, the method may include, via one or more processors,servers, sensors, and/or associated transceivers: receiving (such as viawireless communication or data transmission over one or more radio linksor communication channels) or retrieving an electronic arbitrationdemand associated with the vehicle collision; and/or generating anelectronic recommendation based upon, at least in part, processoranalysis of, or a comparison of, the percentage of fault and theelectronic arbitration demand (such as by using artificial intelligenceor machine learning techniques). The method may include generating a newblock including the electronic recommendation, an indication thereof, orlink thereto or hash thereof, and/or adding the new block to theblockchain.

The telematics, sensor, image, and/or audio data may be generated orcollected by smart infrastructure or by a vehicle not involved in thevehicle collision, but in the vicinity of the vehicle collision locationat the time of the vehicle collision. The telematics, sensor, image,and/or audio data may include telematics data generated and/or collectedby the vehicle, a mobile device traveling within the vehicle, anothervehicle involved in the vehicle collision, and/or another vehicle in thevicinity of the vehicle collision.

Determining a percentage of fault of the vehicle collision for avehicle, vehicle system, or a vehicle driver based upon, at least inpart, processor analysis of the sensor and/or image data collected mayinclude inputting the telematics, sensor, image, and/or audio datacollected into a machine learning algorithm or program, such as a deep,combined, or reinforced or reinforcement learning algorithm or program,programmed or trained to identify a percentage of fault for the vehicle,vehicle system, and/or a driver based upon telematics, sensor, image,and/or audio data.

Determining a vehicle collision occurred based upon, at least in part,processor analysis of the telematics, sensor, image, and/or audio datacollected may include inputting the telematics, sensor, image, and/oraudio data collected into a machine learning algorithm, such as a deep,combined, or reinforced or reinforcement learning algorithm, or programtrained to identify that a vehicle collision occurred based upontelematics, sensor, image, and/or audio data.

The method may include, via one or more processors, servers, sensors,and/or associated transceivers: receiving (such as via wirelesscommunication or data transmission over one or more radio links orcommunication channels) an electronic subrogation demand associated withthe vehicle collision; and/or generating an electronic recommendationbased upon, at least in part, processor analysis of, or processorcomparison of, the percentage of fault and the electronic subrogationdemand (such as by using artificial intelligence or machine learningtechniques). The method may include, via one or more processors,servers, sensors, and/or associated transceivers: generating a new blockincluding the recommendation, or an indication thereof, or a linkthereto, or a hash thereof; and/or adding the new block to theblockchain.

The method may include, via one or more processors, servers, sensors,and/or associated transceivers: generating an electronic subrogationdemand based upon, at least in part, processor analysis of thetelematics, sensor, image, and/or audio data collected and/or thepercentage of fault determined for the vehicle, vehicle system, or thevehicle driver (such as via artificial intelligence or machine learningtechniques); and/or generating a block to add to the blockchain thatincludes a link to, hash of, or an indication of the electronicsubrogation demand. The electronic subrogation demand may include one ormore lines items directed to medical expenses, vehicle repair, towservices, and/or rental vehicle expenses.

The method may include, via one or more processors, servers, sensors,and/or associated transceivers: receiving (such as via wirelesscommunication or data transmission over one or more radio links orcommunication channels) an electronic subrogation demand associated withthe vehicle collision; analyzing the electronic subrogation demand, thetelematics, sensor, image, and/or audio data collected, and/or thepercentage of fault determined for the vehicle, vehicle system, or thevehicle driver (such as by using artificial intelligence and/or machinelearning techniques); generating a recommendation to accept or disputethe subrogation demand; and/or generating a new block including a linkto, or an indication of, the recommendation to add to the blockchain.

The method may include, via one or more processors, servers, sensors,and/or associated transceivers: generating an electronic arbitrationdemand based upon, at least in part, the telematics, sensor, image,and/or audio data collected and/or the percentage of fault determinedfor a vehicle, vehicle system(s), or vehicle driver (such as by usingartificial intelligence and/or machine learning techniques); and/orgenerating a block to add to the blockchain that includes a link to, ahash of, or an indication of, the electronic arbitration demand.

In another aspect, a computer-implemented method of handling aninsurance claim via a shared ledger may be provided. The method mayinclude, via one or more local or remote processors, servers, sensors,and/or associated transceivers: (1) receiving (such as via wirelesscommunication or data transmission over one or more radio links orcommunication channels), gathering, or generating telematics, sensor,image, and/or audio data (such as vehicle-to-vehicle data; smart orautonomous vehicle image, audio, sensor, operational, control decision(e.g., data indicative of control decisions determined or implemented byautonomous or semi-autonomous vehicle systems or technologies),telematics, or other data; mobile device data; smart infrastructuredata; aerial data; vehicle telematics data; smart or interconnected homedata; etc.); (2) determining that a vehicle collision (such as viawireless communication or data transmission over one or more radio linksor communication channels) occurred based upon, at least in part,processor analysis of the telematics, sensor, image, and/or audio datacollected; (3) determining an operational mode for one or more vehiclesbased upon, at least in part, processor analysis of the telematics,sensor, image, and/or audio data collected, the operational modeindicating whether a vehicle (e.g., an autonomous vehicle or anautonomous or semi-autonomous system) or human driver had control ofeach respective vehicle before, during, and/or after the vehiclecollision; (4) determining a percentage of fault, or lack thereof, ofthe vehicle collision for the vehicle, vehicle system, or the humanvehicle driver based upon, at least in part, processor analysis of thetelematics, sensor, image, and/or audio data collected, and whom was incontrol of the vehicle before, during, and/or after the vehiclecollision, respectfully; and/or (5) creating a blockchain for thevehicle collision, or a new block for an existing blockchain, with thetelematics, sensor, image, and/or audio data and/or one or more links tothe telematics, sensor, image, and/or audio data collected, anindication of the operational mode for the vehicle at the time ofvehicle collision, and/or an indication of the percentage of fault(s)determined (for one or more vehicles, vehicle systems, or drivers) tofacilitate blockchain-based claim handling. The method may includeadditional, less, or alternate actions, including those discussedelsewhere herein.

For instance, the method may include, via one or more processors,servers, sensors, and/or associated transceivers: receiving (such as viawireless communication or data transmission over one or more radio linksor communication channels) an electronic arbitration demand associatewith the vehicle collision; and/or generating an electronicrecommendation based upon, at least in part, processor analysis of, orprocessor comparison of, the percentage of fault and the electronicarbitration demand (such as via artificial intelligence and/or machinelearning techniques, including those discussed elsewhere herein). Themethod may include, via one or more processors, servers, sensors, and/orassociated transceivers: generating a new block including therecommendation or a link thereto or hash thereof, and adding the newblock to the blockchain.

In one embodiment, the telematics, sensor, image, and/or audio data maybe generated, collected, or received by smart infrastructure or by avehicle not involved in the vehicle collision in the vicinity of thevehicle collision. Additionally or alternatively, the telematics,sensor, image, and/or audio data may include telematics data collectedby the vehicle, a mobile device traveling within the vehicle, anothervehicle involved in the vehicle collision, and/or another vehicle in thevicinity of the vehicle collision not involved in the vehicle collision.

Determining a percentage of fault of the vehicle collision for one ormore vehicles, vehicle systems, and/or vehicle drivers based upon, atleast in part, processor analysis of the telematics, sensor, image,and/or audio data collected may include inputting the telematics,sensor, image, and/or audio data collected into a machine learningalgorithm or program (such as a deep learning, combined learning,reinforced learning, or reinforcement learning algorithm or program)programmed or trained to identify a percentage of fault for the vehicle,vehicle system, and/or driver based upon telematics, sensor, image,and/or audio data.

Determining a vehicle collision occurred based upon, at least in part,processor analysis of the telematics, sensor, image, and/or audio datacollected may include inputting the telematics, sensor, image, and/oraudio data collected into a machine learning algorithm or program (suchas a deep learning, combined learning, or reinforced learning algorithmor program) trained to identify that a vehicle collision occurred basedupon telematics, sensor, image, and/or audio data.

The method may include, via one or more processors, transceivers,servers, and/or sensors, assigning liability to a manufacturer of thevehicle, vehicle system(s), or the human driver based upon whom or whichsystem had control before, during, and/or after the vehicle collision,and a percentage of fault, or lack thereof, assigned to the vehicle,vehicle system(s), or human driver.

The method may include determining which vehicle, vehicle system(s), orhuman driver had the last clear chance to avoid the vehicle collisionbased upon, at least in part, processor analysis of the telematics,sensor, image, and/or audio data collected. Determining which vehicle,vehicle system(s), or human driver had the last clear chance to avoidthe vehicle collision based upon, at least in part, processor analysisof the telematics, sensor, image, and/or audio data collected mayinclude inputting the telematics, sensor, image, and/or audio data intoa machine learning program trained to identify a party or vehicle, orvehicle system, that had the last clear chance to avoid the vehiclecollision using telematics, sensor, image, and/or audio data.

The method may include determining which vehicle, vehicle system(s),and/or human driver had the last clear chance to avoid the vehiclecollision based upon, at least in part, processor analysis of telematicsdata collected from two or more vehicles involved in the vehiclecollision. Additionally or alternatively, the method may includedetermining which vehicle, vehicle system(s), or human driver had thelast clear chance to avoid the vehicle collision based upon, at least inpart, processor analysis of telematics data collected from one or morevehicles involved in the vehicle collision, and smart infrastructuresensor and/or image data.

Exemplary Computer Systems

In one aspect, a computer system configured to handle an insurance claimvia a shared ledger may be provided. The computer system may include oneor more processors, servers, sensors, and/or associated transceiversconfigured to: (1) receive an electronic notification of a vehiclecollision (such as via wireless communication or data transmission overone or more radio links or communication channels); (2) receive (such asvia wireless communication or data transmission over one or more radiolinks or communication channels) or gather telematics, sensor, image,and/or audio data (such as vehicle-to-vehicle data; smart or autonomousvehicle image, audio, sensor, operational, control decision (e.g., dataindicative of control decisions determined or implemented by autonomousor semi-autonomous vehicle systems or technologies), telematics, orother data; mobile device data; smart infrastructure data; aerial data;vehicle telematics data; smart or interconnected home data; etc.); (3)determine a percentage of fault of the vehicle collision for one or morevehicles or vehicle systems (e.g., for an autonomous or semi-autonomousvehicle or system), and/or vehicle drivers based upon, at least in part,processor analysis of the telematics, sensor, image, and/or audio datacollected, or otherwise generated; and/or (4) create a blockchain forthe vehicle collision, or a new block for an existing blockchain, withone or more links to, or otherwise means for accessing, the telematics,sensor, image, and/or audio data collected, and an indication of thepercentage of fault(s) determined to facilitate blockchain-based claimhandling. The system may include additional, less, or alternatefunctionality, including that discussed elsewhere herein.

For instance, the one or more processors, servers, sensors, and/orassociated transceivers may be further configured to: receive (such asvia wireless communication or data transmission over one or more radiolinks or communication channels) an electronic arbitration demandassociated with the vehicle collision; and/or generate an electronicrecommendation based upon, at least in part, processor analysis of, orprocessor comparison of, the percentage of fault and the electronicarbitration demand. The one or more processors, servers, sensors, and/orassociated transceivers may be further configured to: generate a newblock including the recommendation or a link thereto, or hash thereof orother associated hash; and/or add the new block to the blockchain.

The telematics, sensor, image, and/or audio data may be generated bysmart infrastructure or by a vehicle not involved in the vehiclecollision. The telematics, sensor, image, and/or audio data may includetelematics data collected by the vehicle, a mobile device travelingwithin the vehicle, or another vehicle in the vicinity of the vehiclecollision.

Determining a percentage of fault of the vehicle collision for avehicle, vehicle system, and/or a vehicle driver based upon, at least inpart, processor analysis of the telematics, sensor, image, and/or audiodata collected may include inputting the telematics, sensor, image,and/or audio data collected into a machine learning program (such as adeep, combined, reinforcement, or reinforced learning algorithm orprogram, other type of machine learning program, including thosediscussed elsewhere herein) trained to identify a percentage of faultfor a vehicle, vehicle system, and/or vehicle driver based upontelematics, sensor, image, and/or audio data. The electronicnotification of a vehicle collision may be generated by the vehicle fromprocessor analysis of sensor data generated by one or morevehicle-mounted sensors.

The electronic notification of a vehicle collision may be generated bythe vehicle from processor analysis of image data generated by one ormore vehicle-mounted sensors or cameras. The electronic notification ofa vehicle collision is generated by the vehicle from processor analysisof telematics data (e.g., location, acceleration, speed, cornering, andbraking data from before, during, and after the vehicle collision)generated by one or more vehicle-mounted sensors. The electronicnotification of a vehicle collision may be generated by the vehicle fromprocessor analysis of sensor or audio data generated by one or morevehicle-mounted sensors, and sensor or other data, such as telematicsdata, received from the vehicle and/or one or more nearby vehicles inthe vicinity of the vehicle collision location.

The one or more processors, servers, sensors, and/or associatedtransceivers may be further configured to: receive (such as via wirelesscommunication or data transmission over one or more radio links orcommunication channels) an electronic subrogation demand associated withthe vehicle collision; and/or generate an electronic recommendationbased upon, at least in part, processor analysis of, or processorcomparison of, the percentage of fault and the electronic subrogationdemand.

The one or more processors, servers, sensors, and/or associatedtransceivers may be further configured to: generate a new blockincluding the electronic recommendation, or an indication thereof, or alink thereto (or an associated hash); and/or add the new block to theblockchain.

The one or more processors, servers, sensors, and/or associatedtransceivers may be further configured to: generate an electronicsubrogation demand based upon, at least in part, processor analysis ofthe telematics, sensor, image, and/or audio data collected and/or thepercentage of fault(s) determined for one or more vehicles, vehiclesystems, and/or vehicle drivers (such as via artificial intelligence ormachine learning techniques); and/or generate a block to add to theblockchain that includes a link to or an indication of the electronicsubrogation demand (or an associated hash). The electronic subrogationdemand may include one or more line items directed to medical expenses,vehicle repair costs, vehicle towing services, and/or rental vehicleexpenses.

The one or more processors, servers, sensors, and/or associatedtransceivers may further be configured to: receive (such as via wirelesscommunication or data transmission over one or more radio links orcommunication channels) an electronic subrogation demand associated withthe vehicle collision; analyze the electronic subrogation demand, thetelematics, sensor, image, and/or audio data collected, and/or thepercentage of faults determined for one or more vehicles, vehiclesystems, and/or vehicle drivers (such as by using artificialintelligence and/or machine learning techniques); generate an electronicrecommendation to accept or dispute the subrogation demand; and/orgenerate a new block including a link to, or an indication of (or anassociated hash of), the electronic recommendation to add to theblockchain.

The one or more processors, servers, sensors, and/or associatedtransceivers may be further configured to: generate an electronicarbitration demand based upon, at least in part, the telematics, sensor,image, and/or audio data collected and/or the percentage of fault(s)determined for one or more vehicles, vehicle systems, and/or vehicledrivers; and/or generate a block to add to the blockchain that includesa link to, or an indication of (or an associated hash of), theelectronic arbitration demand.

In another aspect, a computer system configured to handle an insuranceclaim via a shared ledger may be provided. The system may include one ormore processors, servers, sensors, and/or associated transceiversconfigured to: (1) receive (such as via wireless communication or datatransmission over one or more radio links or communication channels),gather, or generate telematics, sensor, image, and/or audio data (suchas vehicle-to-vehicle data; smart or autonomous vehicle image, audio,sensor, operational, control decision (e.g., data indicative of controldecisions determined or implemented by autonomous or semi-autonomousvehicle systems or technologies), telematics, or other data; mobiledevice data; smart infrastructure data; aerial data; vehicle telematicsdata; smart or interconnected home data; etc.); (2) determine that avehicle collision (such as via wireless communication or datatransmission over one or more radio links or communication channels)occurred based upon, at least in part, processor analysis of thetelematics, sensor, image, and/or audio data collected; (3) determine apercentage of fault of the vehicle collision for one or more vehicles,vehicle systems, and/or vehicle drivers based upon, at least in part,processor analysis of the telematics, sensor, image, and/or audio datacollected; and/or (4) create a blockchain for the vehicle collision, ora new block for an existing blockchain, with the telematics, sensor,image, and/or audio data, or one or more links to (or associated hashesof) the telematics, sensor, image, and/or audio data collected, and anindication of the percentage of fault(s) determined to facilitateblockchain-based claim handling. The system may include additional,less, or alternate functionality, including that discussed elsewhereherein.

In another aspect, a computer system configured to handle an insuranceclaim via a shared ledger may be provided. The system may include one ormore processors, servers, sensors, and/or associated transceiversconfigured to: (1) receive (such as via wireless communication or datatransmission over one or more radio links or communication channels),gather, or otherwise generate sensor, image, telematics, and/or audiodata (such as vehicle-to-vehicle data; smart or autonomous vehicleimage, audio, sensor, operational, control decision (e.g., dataindicative of control decisions determined or implemented by autonomousor semi-autonomous vehicle systems or technologies), telematics, orother data; mobile device data; smart infrastructure data; aerial data;vehicle telematics data; smart or interconnected home data; etc.); (2)determine that a vehicle collision (such as via wireless communicationor data transmission over one or more radio links or communicationchannels) occurred based upon, at least in part, processor analysis ofthe sensor, image, telematics, and/or audio data collected; (3)determine an operational mode for one or more vehicles based upon, atleast in part, processor analysis of the sensor, image, telematics,and/or audio data collected, the operational mode indicating whether thevehicle (e.g., an autonomous vehicle), a vehicle system, or a humandriver had control of the vehicle before, during, and/or after thevehicle collision; (4) determine a percentage of fault, or lack thereof,of the vehicle collision for the vehicle, vehicle system, or the humanvehicle driver based upon, at least in part, processor analysis of thesensor, image, telematics, and/or audio data collected, and whom or whatwas in control of the vehicle before, during, and/or after the vehiclecollision, respectfully; and/or (5) create a blockchain for the vehiclecollision, or a new block for an existing blockchain, with the sensor,image, telematics, and/or audio data and/or one or more links to, orhashes of or associated with, the sensor, image, telematics, and/oraudio data collected, an indication of the operational mode for thevehicle at the time of vehicle collision, and/or an indication of thepercentage of fault(s) determined (for the vehicles, vehicle systems,and/or drivers) to facilitate blockchain-based claim handling. Thesystem may include additional, less, or alternate functionality,including that discussed elsewhere herein.

Machine Learning & Other Matters

The computer-implemented methods discussed herein may includeadditional, less, or alternate actions, including those discussedelsewhere herein. The methods may be implemented via one or more localor remote processors, transceivers, servers, and/or sensors (such asprocessors, transceivers, servers, and/or sensors mounted on drones,vehicles or mobile devices, or associated with smart infrastructure orremote servers), and/or via computer-executable instructions stored onnon-transitory computer-readable media or medium.

Additionally, the computer systems discussed herein may includeadditional, less, or alternate functionality, including that discussedelsewhere herein. The computer systems discussed herein may include orbe implemented via computer-executable instructions stored onnon-transitory computer-readable media or medium.

A processor or a processing element may be trained using supervised orunsupervised machine learning, and the machine learning program mayemploy a neural network, which may be a convolutional neural network, adeep learning neural network, deep learning algorithm or program,reinforced or reinforcement learning algorithm or program, or a combinedlearning module or program that learns in two or more fields or areas ofinterest. Machine learning may involve identifying and recognizingpatterns in existing data in order to facilitate making predictions forsubsequent data. Models may be created based upon example inputs inorder to make valid and reliable predictions for novel inputs.

Additionally or alternatively, the machine learning programs may betrained by inputting sample data sets or certain data into the programs,such as drone, autonomous or semi-autonomous drone, image, mobiledevice, vehicle telematics, autonomous vehicle, smart vehicle, smartinfrastructure, aerial or satellite, and/or intelligent home telematicsdata. The machine learning programs may utilize deep or reinforced orreinforcement learning algorithms that may be primarily focused onpattern recognition, and may be trained after processing multipleexamples. The machine learning programs may include Bayesian programlearning (BPL), voice recognition and synthesis, image or objectrecognition, optical character recognition, and/or natural languageprocessing—either individually or in combination. The machine learningprograms may also include natural language processing, semanticanalysis, automatic reasoning, and/or machine learning.

In supervised machine learning, a processing element may be providedwith example inputs and their associated outputs, and may seek todiscover a general rule that maps inputs to outputs, so that whensubsequent novel inputs are provided the processing element may, basedupon the discovered rule, accurately predict the correct output. Inunsupervised machine learning, the processing element may be required tofind its own structure in unlabeled example inputs.

The telematics, sensor, image, audio, or other types of data discussedherein (such as historical data sets associated with a known vehiclecollision and damage) may be collected and input into one or moremachine learning programs to determine a vehicle collision occurred;determine a percentage of fault, of lack thereof, for a vehicle orvehicle system (autonomous vehicle or system) or vehicle driver;determine who or what system was in control of the vehicle during thecollision (the vehicle, vehicle system, or vehicle driver); determine orestimate one or more line items for a subrogation demand or arbitrationdemand; determine or estimate vehicle damage or personal injuriesresulting from the vehicle collision; and/or other functionalitydiscussed herein based upon telematics, sensor, image, audio, and/orother data collected or received that is associated with a present ornew vehicle collision. Once a machine learning algorithm is trainedusing past or historical data, new data received associated with acurrent vehicle collision may be input into the trained machine learningalgorithm, or a processor on which the trained machine learningalgorithm resides, operates, and/or is running.

Additional Exemplary Embodiments

FIG. 15 depicts another computer-implemented method of handling aninsurance claim via a shared ledger, and may include subrogation and/orarbitration aspects. The method 1500 may include, via one or moreprocessors, servers, sensors, and/or associated transceivers: (1)receiving (such as via wireless communication or data transmission overone or more radio links or communication channels) or gathering past orhistorical sensor, telematics, image, and/or audio data (such asvehicle-to-vehicle data; smart or autonomous vehicle image, audio,sensor, operational, control decision (e.g., data indicative of controldecisions determined or implemented by autonomous or semi-autonomousvehicle systems or technologies), telematics, or other data; mobiledevice data; smart infrastructure data; aerial data; vehicle telematicsdata; smart or interconnected home data; etc.) associated with a pastinsurance claim and/or past vehicle collision (having known (i)percentage of fault determined for one or more human drivers orself-driving vehicles or vehicle systems; (ii) vehicle damage; (iii)vehicle repair costs; (iv) personal injuries; (v) rental expenses; (vi)tow expenses; (vii) medical or other expenses; and/or (ix) operationalmode at the time of the collision) (block 1502); (2) inputting the pastor historical sensor, telematics, image, and/or audio data into aprocessor configured with a machine learning algorithm to train theprocessor and/or machine learning algorithm to determine, identify, orestimate (i) a vehicle collision occurred; (ii) a percentage of faultfor one or more human drivers, self-driving vehicles, or autonomous orsemi-autonomous vehicle systems or features; (iii) vehicle damage; (iv)vehicle repair costs; (v) personal injuries; (vi) rental expenses; (vii)tow expenses; (viii) medical expenses; and/or vehicle operational mode(human or machine control) at the time of the collision) (block 1504);(3) receiving (such as via wireless communication or data transmissionover one or more radio links or communication channels) from one or moresources (such as the sources depicted in FIG. 11 ) or gathering new orcurrent sensor, telematics, image, and/or audio data (such asvehicle-to-vehicle data; smart or autonomous vehicle image, audio,sensor, operational, control decision (e.g., data indicative of controldecisions determined or implemented by autonomous or semi-autonomousvehicle systems or technologies), telematics, or other data; mobiledevice data; smart infrastructure data; aerial data; vehicle telematicsdata; smart or interconnected home data; etc.) (block 1506); and/or (4)inputting the new or current sensor, telematics, image, and/or audiodata into the processor configured with the trained machine learningalgorithm to determine or estimate (i) a vehicle collision occurred;(ii) a percentage of fault for one or more human drivers, self-drivingvehicles, or autonomous or semi-autonomous vehicle systems ortechnologies; (iii) vehicle damage; (iv) vehicle repair costs; (v)personal injuries; (vi) rental expenses; (vii) tow expenses; (viii)medical expenses; and/or (ix) vehicle operational mode), wherein thetrained machine learning algorithm may determine that a vehiclecollision occurred; a vehicle operational mode at the time of thecollision; a percentage of fault of the vehicle collision for one ormore vehicles, vehicle systems, and/or vehicle drivers based upon thenew or current sensor, telematics, image, and/or audio data collected orotherwise generated; and/or vehicle damages, medical expenses, or othervehicle collision-related expenses (block 1508).

The method 1500 may also include creating a blockchain for the vehiclecollision, or a new block for an existing blockchain, with the new orcurrent sensor, telematics, image, and/or audio data, or one or morelinks to, or hashes of (or other hashes associated with), the sensor,telematics, image, and/or audio data collected, and/or an indicationthat a vehicle collision occurred, the operational mode, an indicationof the percentage of fault, and the amount of estimated vehicle damagesor other vehicle collision-related expenses determined to facilitateblockchain-based claim handling. The method 1500 may include generatingor receiving a new block or blockchain that includes or indicates anelectronic subrogation demand (block 1510) or electronic arbitrationdemand (block 1512). The machine learning program, such as a deep,combined, reinforced, reinforcement, or other machine learning program,may be further trained to analyze any electronic subrogation orarbitration demands received from another insurance carrier or party,and generate an electronic recommendation to accept, reject, or disputeany portion of the demand, and/or expense related line items therein.The method may include additional, less or alternate actions, includingthose discussed elsewhere herein.

In one aspect, a computer-implemented method of handling an insuranceclaim via a shared ledger may be provided. The method may include, viaone or more processors, servers, sensors, and/or associatedtransceivers: (1) receiving (such as via wireless communication or datatransmission over one or more radio links or communication channels)and/or collecting or gathering past or historical sensor, telematics,image, and/or audio data (such as vehicle-to-vehicle data; smart orautonomous vehicle image, audio, sensor, operational, control decision(e.g., data indicative of control decisions determined or implemented byautonomous or semi-autonomous vehicle systems or technologies),telematics, or other data; mobile device data; smart infrastructuredata; aerial data; vehicle telematics data; smart or interconnected homedata; etc.) associated with a past insurance claim and/or past vehiclecollision (having known (i) percentage of fault determined for one ormore human drivers, self-driving vehicles, and/or vehicle systems; (ii)vehicle damage; (iii) vehicle repair costs; (iv) personal injuries; (v)rental expenses; (vi) tow expenses; and/or (vii) medical expenses); (2)inputting the past or historical sensor, telematics, image, and/or audiodata into a processor configured with a machine learning algorithm totrain the processor and/or machine learning algorithm to determine orestimate (i) a vehicle collision occurred; (ii) percentage of fault forone or more human drivers, self-driving vehicles, or vehicle systems;(iii) vehicle damage; (iv) vehicle repair costs; (v) personal injuries;(vi) rental expenses; (vii) tow expenses; and/or (viii) medicalexpenses); (3) receiving (such as via wireless communication or datatransmission over one or more radio links or communication channels)from one or more sources, or gathering or retrieving, new or currentsensor, telematics, image, and/or audio data (such as vehicle-to-vehicledata; smart or autonomous vehicle image, audio, sensor, operational,control decision (e.g., data indicative of control decisions determinedor implemented by autonomous or semi-autonomous vehicle systems ortechnologies), telematics, or other data; mobile device data; smartinfrastructure data; aerial data; vehicle telematics data; smart orinterconnected home data; etc.); (4) inputting the new or currentsensor, telematics, image, and/or audio data into the processorconfigured with the trained machine learning algorithm to determine orestimate (i) a vehicle collision occurred; (ii) percentage of fault forone or more human drivers, self-driving vehicles, and/or vehiclesystems; (iii) vehicle damage; (iv) vehicle repair costs; (v) personalinjuries; (vi) rental expenses; (vii) tow expenses; and/or (viii)medical expenses), wherein the trained machine learning algorithm atleast determines that a vehicle collision occurred, and a percentage offault of the vehicle collision for one or more vehicles, vehiclesystems, and/or vehicle drivers based upon the new or current sensor,telematics, image, and/or audio data collected or otherwise generated;and/or (5) creating a blockchain for the vehicle collision, or a newblock for an existing blockchain, with the new or current sensor,telematics, image, and/or audio data, or one or more links to thesensor, telematics, image, and/or audio data collected, and/or anindication that a vehicle collision occurred and/or an indication of thepercentage of fault determined to facilitate blockchain-based claimhandling.

The method may include, via one or more processors, servers, sensors,and/or associated transceivers: receiving (such as via wirelesscommunication or data transmission over one or more radio links orcommunication channels) an electronic arbitration demand associated withthe vehicle collision; and/or generating an electronic recommendationbased upon, at least in part, processor analysis of, or a comparison of,the percentage of fault and the electronic arbitration demand (such asby using artificial intelligence or machine learning techniques).

The method may include, via one or more processors, servers, sensors,and/or associated transceivers: receiving (such as via wirelesscommunication or data transmission over one or more radio links orcommunication channels) an electronic subrogation demand associated withthe vehicle collision; and/or inputting the electronic subrogationdemand and the new or current sensor, telematics, audio, and/or imagedata into a machine learning algorithm trained to determine anelectronic recommendation indicating whether to accept, reject, ordispute portions (and/or line items) of the electronic subrogationdemand based upon the electronic subrogation demand and the new orcurrent sensor, telematics, audio, and/or image data. The method mayinclude generating a new block including the electronic recommendation,an indication thereof, or link thereto; and/or adding the new block tothe blockchain.

The new or current sensor, telematics, audio, and/or image data may begenerated by smart infrastructure or by a vehicle not involved in thevehicle collision, but in the vicinity of the vehicle collision locationat the time of the vehicle collision. The new or current sensor,telematics, audio, and/or image data may include telematics datagenerated and/or collected by the vehicle, a mobile device travelingwithin the vehicle, another vehicle involved in the vehicle collision,and/or another vehicle in the vicinity of the vehicle collision.

The machine learning algorithm may be further trained to determinewhether an autonomous vehicle or system, or a human driver was incontrol of the vehicle before, during, and/or after the vehiclecollision based upon, at least in part, processor analysis of the new orcurrent sensor, telematics, audio, and/or image data collected.

The method may include, via one or more processors, servers, sensors,and/or associated transceiver: receiving (such as via wirelesscommunication or data transmission over one or more radio links orcommunication channels) an electronic subrogation demand associated withthe vehicle collision; and/or generating an electronic recommendationbased upon, at least in part, processor analysis of, or processorcomparison of, the percentage of fault and the electronic subrogationdemand (such as by using artificial intelligence or machine learningtechniques).

The method may include, via one or more processors, servers, sensors,and/or associated transceivers: generating a new block including theelectronic recommendation, or an indication thereof, or a link theretoor associated hash; and/or adding the new block to the blockchain.

The method may include, via one or more processors, servers, sensors,and/or associated transceivers: generating an electronic subrogationdemand based upon, at least in part, processor analysis of the sensor,telematics, audio, and/or image data collected and/or the percentage offault determined for the vehicle, vehicle system, and/or the vehicledriver (such as via artificial intelligence or machine learningtechniques); and/or generating a block to add to the blockchain thatincludes a link to, or an indication of, the electronic subrogationdemand. The electronic subrogation demand may include one or more linesitems directed to medical, vehicle repair, tow services, and/or rentalvehicle expenses. Additionally or alternatively, the machine learningalgorithm may be trained to generate or estimate line items associatedwith medical, vehicle repair, towing services, and/or rental vehicleexpenses based upon the new or current sensor, telematics, audio, and/orimage data collected or generated.

The method may include, via one or more processors, servers, sensors,and/or associated transceivers: receiving (such as via wirelesscommunication or data transmission over one or more radio links orcommunication channels) an electronic subrogation demand associated withthe vehicle collision; analyzing the electronic subrogation demand, thesensor, telematics, audio, and/or image data collected, and/or thepercentage of fault determined for one or more vehicles, vehiclesystems, and/or vehicle drivers (such as by using artificialintelligence and/or machine learning techniques); generating arecommendation to accept or dispute the subrogation demand; and/orgenerating a new block including a link to, or an indication of, therecommendation to add to the blockchain.

The method may include, via one or more processors, servers, sensors,and/or associated transceivers: generating an electronic arbitrationdemand based upon, at least in part, the sensor, telematics, audio,and/or image data collected and/or the percentage of fault determinedfor one or more vehicles, vehicle systems, and/or vehicle drivers (suchas by using artificial intelligence and/or machine learning techniques);and/or generating a block to add to the blockchain that includes a linkto or hash thereof, or an indication of, the electronic arbitrationdemand.

The trained machine learning algorithm may be further trained togenerate an electronic arbitration demand based upon, at least in part,the sensor, telematics, audio, and/or image data collected, thepercentage of fault determined for one or more vehicles, vehiclesystems, and/or vehicle drivers, and/or one or more line itemsassociated with vehicle repair or medical expense. The method mayinclude, via one or more processors, servers, sensors, and/or associatedtransceivers generating a block to add to the blockchain that includes alink to, or an indication of, the electronic arbitration demand.

In another aspect, a computer-implemented method of handling aninsurance claim via a shared ledger may be provided. The method mayinclude, via one or more processors, servers, sensors, and/or associatedtransceivers: (1) receiving (such as via wireless communication or datatransmission over one or more radio links or communication channels)and/or collecting or gathering past or historical sensor, telematics,audio, and/or image data (such as vehicle-to-vehicle data; smart orautonomous vehicle image, audio, sensor, operational, control decision(e.g., data indicative of control decisions determined or implemented byautonomous or semi-autonomous vehicle systems or technologies),telematics, or other data; mobile device data; smart infrastructuredata; aerial data; vehicle telematics data; smart or interconnected homedata; etc.) associated with known insurance claims and expenses, and/orknown vehicle collisions (such as associated with vehicle collisionswith known vehicle repair, towing, rental, and/or medical expenses); (2)inputting the past or historical sensor, telematics, audio, and/or imagedata into a machine learning program to train the machine learningprogram to identify, determine or estimate (i) a vehicle collisionoccurred; (ii) a percentage of fault for one or more human drivers,self-driving vehicles, and/or vehicle systems; (iii) an operational modeat the time of the vehicle collision, e.g., whether an autonomousvehicle or system, or human driver was in control of the vehicle before,during, and/or after the vehicle collision; (iv) vehicle damage and/orvehicle repair costs; (v) personal injuries; (vi) rental expenses; (vii)tow expenses; and/or (viii) medical expenses); (3) receiving (such asvia wireless communication or data transmission over one or more radiolinks or communication channels) and/or collecting or gathering new orcurrent sensor, telematics, audio, and/or image data (such asvehicle-to-vehicle data; smart or autonomous vehicle image, audio,sensor, operational, control decision (e.g., data indicative of controldecisions determined or implemented by autonomous or semi-autonomousvehicle systems or technologies), telematics, or other data; mobiledevice data; smart infrastructure data; aerial data; vehicle telematicsdata; smart or interconnected home data; etc.); (4) inputting the new orcurrent sensor, telematics, audio, and/or image into the processorconfigured with the trained machine learning algorithm to identify,determine or estimate (i) a vehicle collision occurred; (ii) apercentage of fault for one or more human drivers, self-drivingvehicles, and/or vehicle systems; (iii) an operational mode at the timeof vehicle collision; (iv) vehicle damage and/or vehicle repair costs;(v) personal injuries; (vi) rental expenses; (vii) tow expenses; and/or(viii) medical expenses), wherein the trained machine learning algorithmat least determines or identifies that a vehicle collision occurred; anoperational mode of the vehicle before, during, and/or after the vehiclecollision; and/or a percentage of fault of the vehicle collision for oneor more vehicles, vehicle systems, and/or vehicle drivers based upon thenew or current sensor, telematics, audio, and/or image data collected;and/or (5) creating a blockchain for the vehicle collision, or a newblock for an existing blockchain, with the sensor, telematics, and/orimage data and/or one or more links to the sensor, telematics, audio,and/or image data collected, an indication of the operational mode forthe vehicle at the time of vehicle collision and/or before, during,and/or after the vehicle collision, and/or an indication of thepercentage of fault determined (for the vehicle, vehicle system, and/orthe driver) to facilitate blockchain-based claim handling.

The machine learning algorithm may be further trained to determine whichvehicle, vehicle system(s), and/or driver involved in the collision hadthe last clear chance to avoid the vehicle collision based upon, atleast in part, the new or current sensor, telematics, audio, and/orimage data.

The machine learning algorithm may be further trained to determinewhether the vehicle, vehicle system, and/or the driver had the lastclear chance to avoid the vehicle collision based upon, at least inpart, the new or current sensor, telematics, audio, and/or image data.Determining whether vehicle, vehicle system, and/or human driver had thelast clear chance to avoid the vehicle collision based upon, at least inpart, processor analysis of the sensor, telematics, audio, and/or imagedata collected may include inputting the past or historical sensor,telematics, audio, and/or image data into a machine learning programtrained to identify a party, vehicle, and/or vehicle system that had thelast clear chance to avoid the vehicle collision using sensor,telematics, audio, and/or image data.

The method may also include determining which vehicle, vehicle system,or human driver had the last clear chance to avoid the vehicle collisionbased upon, at least in part, processor analysis of telematics datacollected from two or more vehicles involved in the vehicle collision.

Additionally or alternatively, the method may include determining whichvehicle, vehicle system, or human driver had the last clear chance toavoid the vehicle collision based upon, at least in part, processoranalysis of telematics data collected from one or more vehicles involvedin the vehicle collision, and smart infrastructure sensor and/or imagedata.

In another aspect, a computer-implemented method of handling aninsurance claim via a shared ledger may be provided. The method mayinclude, via one or more processors, servers, sensors, and/or associatedtransceivers: (1) receiving (such as via wireless communication or datatransmission over one or more radio links or communication channels)and/or collecting or gathering past or historical sensor, telematics,audio, and/or image data (such as vehicle-to-vehicle data; smart orautonomous vehicle image, audio, sensor, operational, control decision(e.g., data indicative of control decisions determined or implemented byautonomous or semi-autonomous vehicle systems or technologies),telematics, or other data; mobile device data; smart infrastructuredata; aerial data; vehicle telematics data; smart or interconnected homedata; etc.) associated with known insurance claims and expenses, and/orknown vehicle collisions (such as associated with vehicle collisionswith known vehicle repair, towing, rental, and/or medical expenses); (2)inputting the past or historical sensor, telematics, audio, and/or imagedata into a machine learning program to train the machine learningprogram to identify, determine or estimate (i) a vehicle collisionoccurred; (ii) a percentage of fault determine for one or more humandrivers, self-driving vehicles, and/or vehicle systems; (iii) anoperational mode at the time of the vehicle collision, e.g., whether anautonomous vehicle or system, or human driver was in control of thevehicle before, during, and/or after the vehicle collision; (iv) vehicledamage and/or vehicle repair costs; (v) personal injuries; (vi) rentalexpenses; (vii) tow expenses; (viii) medical expenses; and/or (ix) asubrogation or arbitration demand associated with the vehiclecollision); (3) receiving (such as via wireless communication or datatransmission over one or more radio links or communication channels) orgathering new or current sensor, telematics, audio, and/or image data(such as vehicle-to-vehicle data; smart or autonomous vehicle image,audio, sensor, operational, control decision (e.g., data indicative ofcontrol decisions determined or implemented by autonomous orsemi-autonomous vehicle systems or technologies), telematics, or otherdata; mobile device data; smart infrastructure data; aerial data;vehicle telematics data; smart or interconnected home data; etc.); (4)inputting the new or current sensor, telematics, audio, and/or imageinto the processor configured with the trained machine learningalgorithm to identify, determine or estimate (i) a vehicle collisionoccurred; (ii) a percentage of fault for one or more human drivers,self-driving vehicles, or vehicle systems; (iii) an operational mode atthe time of vehicle collision; (iv) vehicle damage and/or vehicle repaircosts; (v) personal injuries; (vi) rental expenses; (vii) tow expenses;and/or (viii) medical expenses), wherein the trained machine learningalgorithm at least determines or identifies that a vehicle collisionoccurred; an operational mode of the vehicle before, during, and/orafter the vehicle collision; and a percentage of fault of the vehiclecollision for one or more vehicles, vehicle systems, or vehicle driversbased upon the new or current sensor, telematics, audio, and/or imagedata collected; (5) receiving an electronic subrogation demand generatedby another party for the vehicle collision; (6) comparing the electronicsubrogation demand with the determinations made by, or output of, themachine learning algorithm to accept, reject, or dispute one or moreportions, and/or one or more line items, of the subrogation demand; (7)generating an electronic recommendation to accept, reject, or disputeone or more portions, and/or one or more line items, of the electronicsubrogation demand based upon the comparison; and/or (8) creating ablockchain for the vehicle collision, or a new block for an existingblockchain, with the electronic subrogation demand and/or electronicrecommendation, and/or links thereto or hashes thereof or otherassociated hashes, to facilitate blockchain-based claim handling andcommunicating a response to the subrogation demand.

The electronic recommendation may accept, reject, or dispute a lastclear chance to avoid the vehicle collision (whether for a vehicle,vehicle system, or driver). The electronic recommendation may acceptreject, or dispute one or more line items, the one or more line itemsassociated with: vehicle damages, repair costs, towing expenses, rentalexpenses, personal injuries, and/or medical expenses.

The electronic recommendation may accept, reject, or dispute a vehicleoperational mode at the time of vehicle collision. The electronicrecommendation may accept, reject, or dispute a percentage of fault forthe vehicle collision assigned to a vehicle, vehicle system, or adriver.

The electronic recommendation may accept, reject, or dispute whether oneor more parties, vehicles, or vehicle systems were at least partially atfault. The electronic recommendation may accept, reject, or dispute theparty or parties at fault of causing the vehicle collision.

The electronic recommendation may accept, reject, or dispute whether anautonomous vehicle functionality or technology was at fault, or at leastpartially at fault for causing the vehicle collision. The electronicrecommendation may accept, reject, or dispute which, or which type of,autonomous vehicle functionality or technology was at fault, or at leastpartially at fault for causing the vehicle collision.

The electronic recommendation may accept, reject, or dispute (1) anamount to repair autonomous vehicle functionality or technology damagedduring the vehicle collision; and/or (2) whether an autonomous vehiclefunctionality or technology damaged during the vehicle collision needsto be repaired or replaced.

In another aspect, a computer-implemented method of handling aninsurance claim via a shared ledger may be provided. The method mayinclude, via one or more processors, servers, sensors, and/or associatedtransceivers: (1) receiving (such as via wireless communication or datatransmission over one or more radio links or communication channels)and/or collecting, gathering, or retrieving past or historical sensor,telematics, audio, and/or image data (such as vehicle-to-vehicle data;smart or autonomous vehicle image, audio, sensor, operational, controldecision (e.g., data indicative of control decisions determined orimplemented by autonomous or semi-autonomous vehicle systems ortechnologies), telematics, or other data; mobile device data; smartinfrastructure data; aerial data; vehicle telematics data; smart orinterconnected home data; etc.) associated with known insurance claimsand expenses, and/or known vehicle collisions (such as associated withvehicle collisions with known vehicle repair, towing, rental, and/ormedical expenses); (2) inputting the past or historical sensor,telematics, audio, and/or image data into a machine learning program totrain the machine learning program to identify, determine or estimate(i) a vehicle collision occurred; (ii) a percentage of fault for one ormore human drivers, self-driving vehicles, and/or vehicle systems; (iii)an operational mode at the time of the vehicle collision, e.g., whetheran autonomous vehicle or system, or human driver was in control of thevehicle before, during, and/or after the vehicle collision; (iv) vehicledamage and/or vehicle repair costs; (v) personal injuries; (vi) rentalexpenses; (vii) tow expenses; (viii) medical expenses; and/or (ix) asubrogation or arbitration demand associated with the vehiclecollision); (3) receiving (such as via wireless communication or datatransmission over one or more radio links or communication channels)and/or collecting or gathering new or current sensor, telematics, audio,and/or image data (such as vehicle-to-vehicle data; smart or autonomousvehicle image, audio, sensor, operational, control decision (e.g., dataindicative of control decisions determined or implemented by autonomousor semi-autonomous vehicle systems or technologies), telematics, orother data; mobile device data; smart infrastructure data; aerial data;vehicle telematics data; smart or interconnected home data; etc.); (4)inputting the new or current sensor, telematics, audio, and/or imageinto the processor configured with the trained machine learningalgorithm to identify, determine or estimate (i) a vehicle collisionoccurred; (ii) a percentage of fault for one or more human drivers,self-driving vehicles, or vehicle systems; (iii) an operational mode atthe time of vehicle collision; (iv) vehicle damage and/or vehicle repaircosts; (v) personal injuries; (vi) rental expenses; (vii) tow expenses;and/or (viii) medical expenses), wherein the trained machine learningalgorithm at least determines or identifies that a vehicle collisionoccurred; an operational mode of the vehicle before, during, and/orafter the vehicle collision; and a percentage of fault of the vehiclecollision for one or more vehicles, vehicle systems, and/or vehicledrivers based upon the new or current sensor, telematics, audio, and/orimage data collected; (5) receiving an electronic arbitration demandgenerated by another party for the vehicle collision; (6) comparing theelectronic subrogation demand with the determinations made by, or outputof, the machine learning algorithm to accept, reject, or dispute one ormore portions, and/or one or more line items, of the electronicarbitration demand; (7) generating an electronic recommendation toaccept, reject, or dispute one or more portions, and/or one or more lineitems, of the electronic arbitration demand based upon the comparison;and/or (8) creating a blockchain for the vehicle collision, or a newblock for an existing blockchain, with the electronic arbitration and/orthe electronic recommendation, or links thereto or hashes thereof (orother associated hashes), to facilitate blockchain-based claim handlingand communicating a response to the electronic arbitration demand. Theforegoing computer-implemented methods may include additional, less, oralternate actions, including those discussed elsewhere herein.

In another aspect, a computer system configured to handle an insuranceclaim via a shared ledger may be provided. The system may include one ormore processors, servers, sensors, and/or associated transceiversconfigured to: (1) receive (such as via wireless communication or datatransmission over one or more radio links or communication channels)and/or gather past or historical sensor, telematics, audio, and/or imagedata (such as vehicle-to-vehicle data; smart or autonomous vehicleimage, audio, sensor, operational, control decision (e.g., dataindicative of control decisions determined or implemented by autonomousor semi-autonomous vehicle systems or technologies), telematics, orother data; mobile device data; smart infrastructure data; aerial data;vehicle telematics data; smart or interconnected home data; etc.)associated with a past insurance claim and/or past vehicle collision(having known (i) percentage of fault for one or more human drivers,self-driving vehicles, and/or vehicle systems; (ii) vehicle damage;(iii) vehicle repair costs; (iv) personal injuries; (v) rental expenses;(vi) tow expenses; and/or (vii) medical expenses); (2) input the past orhistorical sensor, telematics, audio, and/or image data into a processorconfigured with a machine learning algorithm to train the processorand/or machine learning algorithm to determine or estimate (i) a vehiclecollision occurred; (ii) percentage of fault for one or more humandrivers, self-driving vehicles, and/or vehicle systems; (iii) vehicledamage; (iv) vehicle repair costs; (v) personal injuries; (vi) rentalexpenses; (vii) tow expenses; and/or (viii) medical expenses); (3)receive (such as via wireless communication or data transmission overone or more radio links or communication channels) and/or collect orgather new or current sensor, telematics, audio, and/or image data (suchas vehicle-to-vehicle data; smart or autonomous vehicle image, audio,sensor, operational, control decision (e.g., data indicative of controldecisions determined or implemented by autonomous or semi-autonomousvehicle systems or technologies), telematics, or other data; mobiledevice data; smart infrastructure data; aerial data; vehicle telematicsdata; smart or interconnected home data; etc.); (4) input the new orcurrent sensor, telematics, audio, and/or image data into the processorconfigured with the trained machine learning algorithm to determine orestimate (i) a vehicle collision occurred; (ii) percentage of fault forone or more human drivers, self-driving vehicles, or vehicle systems;(iii) vehicle damage; (iv) vehicle repair costs; (v) personal injuries;(vi) rental expenses; (vii) tow expenses; and/or (viii) medicalexpenses), wherein the trained machine learning algorithm at leastdetermines that a vehicle collision occurred, and a percentage of faultof the vehicle collision for a vehicle, vehicle system, and/or a vehicledriver based upon the new or current sensor, telematics, audio, and/orimage data collected; and/or (5) create a blockchain for the vehiclecollision, or a new block for an existing blockchain, with the new orcurrent sensor, telematics, audio, and/or image data, or one or morelinks to the sensor, telematics, audio, and/or image data collected,and/or an indication that a vehicle collision occurred and/or anindication of the percentage of fault determined to facilitateblockchain-based claim handling. The system may be further configured togenerate and/or analyze electronic subrogation or arbitration demands.

In another aspect, a computer system configured to handle an insuranceclaim via a shared ledger may be provided. The system may include one ormore processors, servers, sensors, and/or associated transceiversconfigured to: (1) receive (such as via wireless communication or datatransmission over one or more radio links or communication channels)and/or gather or retrieve past or historical sensor, telematics, audio,and/or image data (such as vehicle-to-vehicle data; smart or autonomousvehicle image, audio, sensor, operational, control decision (e.g., dataindicative of control decisions determined or implemented by autonomousor semi-autonomous vehicle systems or technologies), telematics, orother data; mobile device data; smart infrastructure data; aerial data;vehicle telematics data; smart or interconnected home data; etc.)associated with known insurance claims and expenses, and/or knownvehicle collisions (such as associated with vehicle collisions withknown vehicle repair, towing, rental, and/or medical expenses); (2)input the past or historical sensor, telematics, audio, and/or imagedata into a machine learning program to train the machine learningprogram to identify, determine or estimate (i) a vehicle collisionoccurred; (ii) percentage of fault for one or more human drivers,self-driving vehicles, and/or vehicle systems; (iii) an operational modeat the time of the vehicle collision, e.g., whether an autonomousvehicle or system, or human driver was in control of the vehicle before,during, and/or after the vehicle collision; (iv) vehicle damage and/orvehicle repair costs; (v) personal injuries; (vi) rental expenses; (vii)tow expenses; and/or (viii) medical expenses); (3) receive (such as viawireless communication or data transmission over one or more radio linksor communication channels) and/or gather new or current sensor,telematics, audio, and/or image data (such as vehicle-to-vehicle data;smart or autonomous vehicle image, audio, sensor, operational, controldecision (e.g., data indicative of control decisions determined orimplemented by autonomous or semi-autonomous vehicle systems ortechnologies), telematics, or other data; mobile device data; smartinfrastructure data; aerial data; vehicle telematics data; smart orinterconnected home data; etc.); (4) input the new or current sensor,telematics, audio, and/or image into the processor configured with thetrained machine learning algorithm to identify, determine or estimate(i) a vehicle collision occurred; (ii) percentage of fault for one ormore human drivers, self-driving vehicles, and/or vehicle systems; (iii)an operational mode at the time of vehicle collision; (iv) vehicledamage and/or vehicle repair costs; (v) personal injuries; (vi) rentalexpenses; (vii) tow expenses; and/or (viii) medical expenses), whereinthe trained machine learning algorithm at least determines or identifiesthat a vehicle collision occurred; an operational mode of the vehiclebefore, during, and/or after the vehicle collision; and a percentage offault of the vehicle collision for one or more vehicles, vehiclesystems, and/or vehicle drivers based upon the new or current sensor,telematics, audio, and/or image data collected; and/or (5) create ablockchain for the vehicle collision, or a new block for an existingblockchain, with the sensor, telematics, audio, and/or image data and/orone or more links to the sensor, telematics, audio, and/or image datacollected, an indication of the operational mode for the vehicle at thetime of vehicle collision and/or before, during, and/or after thevehicle collision, and/or an indication of the percentage of faultdetermined (for the vehicles, vehicle systems, and/or drivers) tofacilitate blockchain-based claim handling.

In another aspect, a computer system configured to handle an insuranceclaim via a shared ledger may be provided. The system may include one ormore processors, servers, sensors, and/or associated transceiversconfigured to: (1) receive (such as via wireless communication or datatransmission over one or more radio links or communication channels)and/or collect, retrieve, or gather past or historical sensor,telematics, audio, and/or image data (such as vehicle-to-vehicle data;smart or autonomous vehicle image, audio, sensor, operational, controldecision (e.g., data indicative of control decisions determined orimplemented by autonomous or semi-autonomous vehicle systems ortechnologies), telematics, or other data; mobile device data; smartinfrastructure data; aerial data; vehicle telematics data; smart orinterconnected home data; etc.) associated with known insurance claimsand expenses, and/or known vehicle collisions (such as associated withvehicle collisions with known vehicle repair, towing, rental, and/ormedical expenses); (2) input the past or historical sensor, telematics,audio, and/or image data into a machine learning program to train themachine learning program to identify, determine or estimate (i) avehicle collision occurred; (ii) a percentage of fault for one or morehuman drivers, self-driving vehicles, and/or vehicle systems orcomponents; (iii) an operational mode at the time of the vehiclecollision, e.g., whether an autonomous vehicle or system, or humandriver was in control of the vehicle before, during, and/or after thevehicle collision; (iv) vehicle damage and/or vehicle repair costs; (v)personal injuries; (vi) rental expenses; (vii) tow expenses; (viii)medical expenses; and/or (ix) a subrogation or arbitration demandassociated with the vehicle collision); (3) receive (such as viawireless communication or data transmission over one or more radio linksor communication channels) and/or gather new or current sensor,telematics, audio, and/or image data (such as vehicle-to-vehicle data;smart or autonomous vehicle image, audio, sensor, operational, controldecision (e.g., data indicative of control decisions determined orimplemented by autonomous or semi-autonomous vehicle systems ortechnologies), telematics, or other data; mobile device data; smartinfrastructure data; aerial data; vehicle telematics data; smart orinterconnected home data; etc.); (4) input the new or current sensor,telematics, audio, and/or image into the processor configured with thetrained machine learning algorithm to identify, determine or estimate(i) a vehicle collision occurred; (ii) a percentage of fault for one ormore human drivers, self-driving vehicles, and/or vehicle systems; (iii)an operational mode at the time of vehicle collision; (iv) vehicledamage and/or vehicle repair costs; (v) personal injuries; (vi) rentalexpenses; (vii) tow expenses; and/or (viii) medical expenses), whereinthe trained machine learning algorithm at least determines or identifiesthat a vehicle collision occurred; an operational mode of the vehiclebefore, during, and/or after the vehicle collision; and a percentage offault of the vehicle collision for one or more vehicles, vehicle systemsor components, and/or vehicle drivers based upon the new or currentsensor, telematics, audio, and/or image data collected; (5) receive anelectronic subrogation demand generated by another party for the vehiclecollision; (6) compare the electronic subrogation demand with thedeterminations made by, or output of, the machine learning algorithm toaccept, reject, or dispute one or more portions, and/or one or more lineitems, of the subrogation demand; (7) generate an electronicrecommendation to accept, reject, or dispute one or more portions,and/or one or more line items, of the subrogation demand based upon thecomparison; and/or (8) create a blockchain for the vehicle collision, ora new block for an existing blockchain, with the electronic subrogationdemand and/or electronic recommendation, and/or links thereto or hashesthereof (or other associated hashes), to facilitate blockchain-basedclaim handling and communicating a response to the subrogation demand.

In another aspect, a computer system configured to handle or process aninsurance claim via a shared ledger may be provided. The system mayinclude one or more processors, servers, sensors, and/or associatedtransceivers configured to: (1) receive (such as via wirelesscommunication or data transmission over one or more radio links orcommunication channels) and/or gather, retrieve, or collect past orhistorical sensor, telematics, audio, and/or image data (such asvehicle-to-vehicle data; smart or autonomous vehicle image, audio,sensor, operational, control decision (e.g., data indicative of controldecisions determined or implemented by autonomous or semi-autonomousvehicle systems or technologies), telematics, or other data; mobiledevice data; smart infrastructure data; aerial data; vehicle telematicsdata; smart or interconnected home data; etc.) associated with knowninsurance claims and expenses, and/or known vehicle collisions (such asassociated with vehicle collisions with known vehicle repair, towing,rental, and/or medical expenses); (2) input the past or historicalsensor, telematics, audio, and/or image data into a machine learningprogram to train the machine learning program to identify, determine orestimate (i) a vehicle collision occurred; (ii) a percentage of faultdetermine for one or more human drivers, self-driving vehicles, vehiclesystems, and/or vehicle components; (iii) an operational mode at thetime of the vehicle collision, e.g., whether an autonomous vehicle orsystem, or human driver was in control of the vehicle before, during,and/or after the vehicle collision; (iv) vehicle damage and/or vehiclerepair costs; (v) personal injuries; (vi) rental expenses; (vii) towexpenses; (viii) medical expenses; and/or (ix) a subrogation orarbitration demand associated with the vehicle collision); (3) receive(such as via wireless communication or data transmission over one ormore radio links or communication channels) and/or collect, retrieve, orgather new or current sensor, telematics, audio, and/or image data (suchas vehicle-to-vehicle data; smart or autonomous vehicle image, audio,sensor, operational, control decision (e.g., data indicative of controldecisions determined or implemented by autonomous or semi-autonomousvehicle systems or technologies), telematics, or other data; mobiledevice data; smart infrastructure data; aerial data; vehicle telematicsdata; smart or interconnected home data; etc.); (4) input the new orcurrent sensor, telematics, audio, and/or image into the processorconfigured with the trained machine learning algorithm to identify,determine or estimate (i) a vehicle collision occurred; (ii) apercentage of fault for one or more human drivers, self-drivingvehicles, vehicles systems, and/or vehicle components; (iii) anoperational mode at the time of vehicle collision; (iv) vehicle damageand/or vehicle repair costs; (v) personal injuries; (vi) rentalexpenses; (vii) tow expenses; and/or (viii) medical expenses), whereinthe trained machine learning algorithm at least determines or identifiesthat a vehicle collision occurred; an operational mode of the vehiclebefore, during, and/or after the vehicle collision; and a percentage offault of the vehicle collision for one or more vehicles, vehicle systemsor components, and/or vehicle drivers based upon the new or currentsensor, telematics, and/or image data collected; (5) receive anelectronic arbitration demand generated by another party for the vehiclecollision; (6) compare the electronic subrogation demand with thedeterminations made by, or output of, the machine learning algorithm toaccept, reject, or dispute one or more portions, and/or one or more lineitems, of the electronic arbitration demand; (7) generate an electronicrecommendation to accept, reject, or dispute one or more portions,and/or one or more line items, of the electronic arbitration demandbased upon the comparison; and/or (8) create a blockchain for thevehicle collision, or a new block for an existing blockchain, with theelectronic arbitration demand and/or the electronic recommendation, orlinks thereto or hashes thereof, to facilitate blockchain-based claimhandling and communicating a response to the electronic arbitrationdemand.

The foregoing computer systems may include additional, less, oralternate functionality, including that discussed elsewhere herein.

Additional Considerations

This detailed description is to be construed as exemplary only and doesnot describe every possible embodiment, as describing every possibleembodiment would be impractical, if not impossible. One may be implementnumerous alternate embodiments, using either current technology ortechnology developed after the filing date of this application.

An authoritative, trusted, immutable, distributed, shareable, securesystem may be needed to record if a human driver is controlling avehicle, and/or if the vehicle is acting autonomously orsemi-autonomously. The record may include crash sensor data to recordcrash information correlating to driver control information.

Blockchain technology may be used to store the transactions of controlinstances (from autonomous to human control to autonomous, for example).These control instances may be stored as they occur into blocks.Accordingly, this data may be included into the distributed ledgerenvironment of the blockchain. In this environment, a consensus systemmay fix the events/blocks immutably and securely.

As noted herein, after collection of the information regarding thevehicle by one or more nodes within a communication network, atransaction (and/or new block) including the vehicle informationcollected may be broadcast to the blockchain, and/or a new blockverified and then added to the blockchain to reflect an updated state ofthe vehicle. For each of the computer-implemented methods discussedherein, in one embodiment, a transaction and/or new block may begenerated and then broadcast to the blockchain network for verificationonce vehicle data, and/or new sensor or other data, have been generatedand/or collected by one or more nodes within the communication network.As such, tracking the status of a vehicle may be more reliable and/orfraud-resistant as each node may include a proof-of-identity in itstransaction modifying the state of the vehicle and/or vehicle-relatedblocks or blockchain.

Further, with the computer-implemented methods discussed herein, networkparticipants may function as full nodes that validate and/or generatenew blocks and transactions, and/or compile transactions into blocksthat are then added to the network. However, not all participants needbe nodes that compile transactions into blocks, and/or validatetransactions and blocks received from other network participants—as somenetwork participants may wish to rely on other network nodes to providecomputer processing and/or storage services that enable usage of thesystem or blockchain.

In some scenarios, the blockchain may have public interfaces that allowvisibility into the data. In one embodiment, a private blockchaininterface may also be used by auto manufacturers, law enforcement,insurers, and regulatory agencies.

An element of smart contracts may also be enabled in the system.Depending on the sequence of events in the blockchain, terms of thesmart contract may be executed immediately, such as sending a tow truckto the geolocation if tow assistance is a part of the policy, filing alegal action by a subrogation team of an insurer is brought against anauto manufacturer (for example, if an accident occurs when theautonomous vehicle was in autonomous control), conducting a policyreview, filing a police report request with the jurisdiction of theroadway, processing claims awards made (for example, a partial paymentif deductible is met, to handle car rental or minor medical expense),sending a renewal notice for the policy, and so on.

In some aspects, customers may opt-in to a rewards, loyalty, or otherprogram. The customer may allow a remote server, such as an enforcementserver, to collect sensor, telematics, image, audio, smart or autonomousvehicle, mobile device, smart or interconnected home, and other types ofdata discussed herein. With customer permission or affirmative consent,the data collected may be analyzed to provide certain benefits tocustomers. For instance, insurance cost savings may be provided to lowerrisk or risk averse customers. Discounts, including cryptocurrency, maybe awarded to accounts associated with the customer. The otherfunctionality discussed herein may also be provided to customers inreturn for them allowing collection and analysis of the types of datadiscussed herein, as well as participating in the validation of the datadiscussed herein.

Further to this point, although the embodiments described herein oftenutilize credit report information as an example of sensitiveinformation; the embodiments described herein are not limited to suchexamples. Instead, the embodiments described herein may be implementedin any suitable environment in which it is desirable to identify andcontrol specific type of information. As part of implementing theautomotive claims process, vehicle loss history, and the lifecycle of aVehicle Identification Number, a financial institution may be a part ofthe process. For example, the aforementioned embodiments may beimplemented by the financial institution to identify and contain bankaccount statements, brokerage account statements, tax documents, etc. Toprovide another example, the aforementioned embodiments may beimplemented by a lender to not only identify, re-route, and quarantinecredit report information, but to apply similar techniques to preventthe dissemination of loan application documents that are preferablydelivered to a client for signature in accordance with a more securemeans (e.g., via a secure login to a web server) than via email.

With the foregoing, a user may be an insurance customer who may opt-into rewards, insurance discount, or other type of program. After theinsurance customer provides their affirmative consent, an insuranceprovider remote server may collect data from the customer's mobiledevice, smart home controller, smart or autonomous vehicle, or othersmart devices—such as with the customer's permission or affirmativeconsent. The data collected may be related to smart or autonomousvehicle functionality, smart home functionality (or home occupantpreferences or preference profiles), smart or autonomous vehiclefunctionality, and/or insured assets before (and/or after) aninsurance-related event, including those events discussed elsewhereherein. In return, risk averse insureds, vehicle owners, home owners, orhome, apartment, or vehicle occupants may receive discounts or insurancecost savings related to home, renters, personal articles, auto, mobile,health, life, and other types of insurance from the insurance provider.

In one aspect, smart or autonomous vehicle data, smart or interconnectedhome data, and/or other data, including the types of data discussedelsewhere herein, may be collected or received by an insurance providerremote server, such as via direct or indirect wireless communication ordata transmission from a smart or autonomous vehicle, smart homecontroller, mobile device, or other customer computing device, after acustomer affirmatively consents or otherwise opts-in to an insurancediscount, reward, or other program. The insurance provider may thenanalyze the data received with the customer's permission to providebenefits to the customer. As a result, risk averse customers may receiveinsurance discounts or other insurance cost savings based upon data thatreflects low risk behavior and/or technology that mitigates or preventsrisk to (i) insureds or insured assets, such as homes, personalbelongings, or vehicles, and/or (ii) home, apartment, or vehicleoccupants.

Furthermore, although the present disclosure sets forth a detaileddescription of numerous different embodiments, it should be understoodthat the legal scope of the description is defined by the words of theclaims set forth at the end of this patent and equivalents. The detaileddescription is to be construed as exemplary only and does not describeevery possible embodiment since describing every possible embodimentwould be impractical. Numerous alternative embodiments may beimplemented, using either current technology or technology developedafter the filing date of this patent, which would still fall within thescope of the claims. Although the following text sets forth a detaileddescription of numerous different embodiments, it should be understoodthat the legal scope of the description is defined by the words of theclaims set forth at the end of this patent and equivalents. The detaileddescription is to be construed as exemplary only and does not describeevery possible embodiment since describing every possible embodimentwould be impractical. Numerous alternative embodiments may beimplemented, using either current technology or technology developedafter the filing date of this patent, which would still fall within thescope of the claims.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Additionally, certain embodiments are described herein as includinglogic or a number of routines, subroutines, applications, orinstructions. These may constitute either software (e.g., code embodiedon a machine-readable medium or in a transmission signal) or hardware.In hardware, the routines, etc., are tangible units capable ofperforming certain operations and may be configured or arranged in acertain manner. In exemplary embodiments, one or more computer systems(e.g., a standalone, client or server computer system) or one or morehardware modules of a computer system (e.g., a processor or a group ofprocessors) may be configured by software (e.g., an application orapplication portion) as a hardware module that operates to performcertain operations as described herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. For example, a hardware module maycomprise dedicated circuitry or logic that is permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC)) toperform certain operations. A hardware module may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that istemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

Accordingly, the term “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. Considering embodiments inwhich hardware modules are temporarily configured (e.g., programmed),each of the hardware modules need not be configured or instantiated atany one instance in time. For example, where the hardware modulescomprise a general-purpose processor configured using software, thegeneral-purpose processor may be configured as respective differenthardware modules at different times. Software may accordingly configurea processor, for example, to constitute a particular hardware module atone instance of time and to constitute a different hardware module at adifferent instance of time.

Hardware modules may provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multipleof such hardware modules exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the hardware modules. In embodiments in whichmultiple hardware modules are configured or instantiated at differenttimes, communications between such hardware modules may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware modules have access. Forexample, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and may operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processor-implemented. For example, at least some of theoperations of a method may be performed by one or more processors orprocessor-implemented hardware modules. The performance of certain ofthe operations may be distributed among the one or more processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location (e.g., within a home environment, anoffice environment or as a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment.

Some embodiments may be described using the expression “coupled” and“connected” along with their derivatives. For example, some embodimentsmay be described using the term “coupled” to indicate that two or moreelements are in direct physical or electrical contact. The term“coupled,” however, may also mean that two or more elements are not indirect contact with each other, but yet still co-operate or interactwith each other. The embodiments are not limited in this context.

As used herein, the terms “includes,” “comprising,” “including,” “has,”“having” or any other variation thereof, are intended to cover anon-exclusive inclusion. For example, a process, method, article, orapparatus that includes a list of elements is not necessarily limited toonly those elements but may include other elements not expressly listedor inherent to such process, method, article, or apparatus. Further,unless expressly stated to the contrary, “or” refers to an inclusive orand not to an exclusive or. For example, a condition A or B is satisfiedby any one of the following: A is true (or present) and B is false (ornot present), A is false (or not present) and B is true (or present),and both A and B are true (or present).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the description. Thisdescription, and the claims that follow, should be read to include oneor at least one and the singular also includes the plural unless it isobvious that it is meant otherwise.

The patent claims at the end of this patent application are not intendedto be construed under 35 U.S.C. § 112(f) unless traditionalmeans-plus-function language is expressly recited, such as “means for”or “step for” language being explicitly recited in the claim(s).

What is claimed:
 1. A computer-implemented method of handling aninsurance claim via a shared ledger, the method comprising: receiving,at one or more processors, historical sensor data associated with a pastvehicle collision; inputting, at the one or more processors, thehistorical sensor data into an algorithm, the algorithm being a machinelearning algorithm that is trained by the historical sensor data todetermine a percentage of fault for human drivers or self-drivingvehicles; receiving, at the one or more processors, current sensor dataassociated with a vehicle collision; inputting, at the one or moreprocessors, the current sensor data into the machine learning algorithmto determine a percentage of fault of the current vehicle collision fora human driver or a self-driving vehicle; adding, at the one or moreprocessors, a block to a blockchain with an indication of the determinedpercentage of fault determined by the trained machine learningalgorithm; and automatically processing and/or adjusting, at the one ormore processors, an insurance claim via the blockchain.
 2. Thecomputer-implemented method of claim 1, further comprising: receiving,at the one or more processors, an electronic arbitration demandassociated with the vehicle collision; and generating, at the one ormore processors, a recommendation based upon, at least in part, analysisof the percentage of fault and the electronic arbitration demand.
 3. Thecomputer-implemented method of claim 2, further comprising: generating,at the one or more processors, a new block including the recommendationor a link thereto; and adding, at the one or more processors, the newblock to the blockchain.
 4. The computer-implemented method of claim 1,wherein the sensor data is generated by smart infrastructure or by avehicle not involved in the vehicle collision.
 5. Thecomputer-implemented method of claim 1, wherein the sensor data includestelematics data collected by another vehicle in the vicinity of thevehicle collision.
 6. The computer-implemented method of claim 1,further comprising: receiving, at the one or more processors, anelectronic notification of the vehicle collision generated by thevehicle from analysis of sensor data generated by one or morevehicle-mounted sensors.
 7. The computer-implemented method of claim 1,further comprising: receiving, at the one or more processors, anelectronic notification of the vehicle collision generated by thevehicle from analysis of image data generated by one or morevehicle-mounted sensors or cameras.
 8. The computer-implemented methodof claim 1, further comprising: receiving, at the one or moreprocessors, an electronic notification of the vehicle collisiongenerated by the vehicle from analysis of telematics data generated byone or more vehicle-mounted sensors.
 9. The computer-implemented methodof claim 1, further comprising: receiving, at the one or moreprocessors, an electronic notification of the vehicle collisiongenerated by the vehicle from analysis of sensor data generated by oneor more vehicle-mounted sensors, and sensor or other data, such astelematics data, received from the vehicle and one or more nearbyvehicles in the vicinity of the vehicle collision location.
 10. Acomputer-implemented method of handling an insurance claim via a sharedledger, the method comprising: receiving, at one or more processors,historical sensor data associated with a past vehicle collision;inputting, at the one or more processors, the historical sensor datainto an algorithm, the algorithm being a machine learning algorithm thatis trained by the historical sensor data to: (i) determine a percentageof fault for human drivers or self-driving vehicles, and (ii) determinedata relevant to a past vehicle collision; receiving, at the one or moreprocessors, current sensor data associated with a current vehiclecollision; inputting, at the one or more processors, the current sensordata into the machine learning algorithm to determine: (i) that avehicle was under autonomous control before, during, and/or after thecurrent vehicle collision, and (ii) a percentage of fault for thevehicle determined to be under autonomous control; adding, at the one ormore processors, a block to a blockchain with an indication of thedetermined percentage of fault for the vehicle determined to be underautonomous control; and automatically processing and/or adjusting, atthe one or more processors, an insurance claim via the blockchain. 11.The computer-implemented method of claim 10, further comprising:receiving, at the one or more processors, an electronic arbitrationdemand associated with the vehicle collision; and generating, at the oneor more processors, a recommendation based upon, at least in part,analysis of the percentage of fault and the electronic arbitrationdemand.
 12. The computer-implemented method of claim 11, furthercomprising: generating, at the one or more processors, a new blockincluding the recommendation or a link thereto; and adding, at the oneor more processors, the new block to the blockchain.
 13. Thecomputer-implemented method of claim 10, wherein the sensor data isgenerated by smart infrastructure or by a vehicle not involved in thevehicle collision.
 14. The computer-implemented method of claim 10,wherein the sensor data includes telematics data collected by thevehicle, a mobile device traveling within the vehicle, another vehiclein the vicinity of the vehicle collision, or combinations thereof.
 15. Acomputer system for providing data relevant to collisions andsubrogation claims by interacting with a distributed ledger maintainedby a plurality of participants, the system comprising: a networkinterface configured to interface with one or more processors; one ormore sensors; a memory configured to store non-transitory computerexecutable instructions and configured to interface with the one or moreprocessors; and the one or more processors configured to interface withthe memory, wherein the one or more processors are configured to executethe non-transitory computer executable instructions to cause the one ormore processors to: receive historical sensor data associated with apast vehicle collision; input the historical sensor data into a machinelearning algorithm to train the machine learning algorithm to determinea percentage of fault for human drivers or self-driving vehicles;receive current sensor data associated with a current vehicle collision;input the current sensor data into the machine learning algorithm todetermine a percentage of fault of the current vehicle collision for ahuman driver or a self-driving vehicle; add a block to a blockchain withan indication of the determined percentage of fault determined by thetrained machine learning algorithm; and automatically process and/oradjust, at the one or more processors, an insurance claim via theblockchain.
 16. The system of claim 15, wherein the one or moreprocessors are further configured to execute the non-transitory computerexecutable instructions to cause the one or more processors to: receivean electronic arbitration demand associated with the vehicle collision;and generate a recommendation based upon, at least in part, analysis ofthe percentage of fault and the electronic arbitration demand.
 17. Thesystem of claim 16, wherein the one or more processors are furtherconfigured to execute the non-transitory computer executableinstructions to cause the one or more processors to: generate a newblock including the recommendation or a link thereto; and add the newblock to the blockchain.
 18. The system of claim 15, wherein the sensordata is generated by smart infrastructure or by a vehicle not involvedin the vehicle collision.
 19. The system of claim 15, wherein the one ormore processors are further configured to execute the non-transitorycomputer executable instructions to cause the one or more processors to:receive an electronic notification of the vehicle collision generated bythe vehicle from analysis of sensor data generated by one or morevehicle-mounted sensors.
 20. The system of claim 15, wherein: theprocessor is further configured to execute the non-transitory computerexecutable instructions to cause the processor to add, to theblockchain, an additional block including an electronic subrogationdemand; the electronic subrogation demand is based on the determinedpercentage of fault, and wherein the electronic subrogation demandincludes one or more line items directed to medical expenses; and theprocessor is further configured to execute the non-transitory computerexecutable instructions to cause the processor to add, to theblockchain, a block including a hash of the electronic subrogationdemand.